--- _id: '10803' abstract: - lang: eng text: Given the abundance of applications of ranking in recent years, addressing fairness concerns around automated ranking systems becomes necessary for increasing the trust among end-users. Previous work on fair ranking has mostly focused on application-specific fairness notions, often tailored to online advertising, and it rarely considers learning as part of the process. In this work, we show how to transfer numerous fairness notions from binary classification to a learning to rank setting. Our formalism allows us to design methods for incorporating fairness objectives with provable generalization guarantees. An extensive experimental evaluation shows that our method can improve ranking fairness substantially with no or only little loss of model quality. article_number: '2102.05996' article_processing_charge: No author: - first_name: Nikola H full_name: Konstantinov, Nikola H id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87 last_name: Konstantinov - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0002-4561-241X citation: ama: Konstantinov NH, Lampert C. Fairness through regularization for learning to rank. arXiv. doi:10.48550/arXiv.2102.05996 apa: Konstantinov, N. H., & Lampert, C. (n.d.). Fairness through regularization for learning to rank. arXiv. https://doi.org/10.48550/arXiv.2102.05996 chicago: Konstantinov, Nikola H, and Christoph Lampert. “Fairness through Regularization for Learning to Rank.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2102.05996. ieee: N. H. Konstantinov and C. Lampert, “Fairness through regularization for learning to rank,” arXiv. . ista: Konstantinov NH, Lampert C. Fairness through regularization for learning to rank. arXiv, 2102.05996. mla: Konstantinov, Nikola H., and Christoph Lampert. “Fairness through Regularization for Learning to Rank.” ArXiv, 2102.05996, doi:10.48550/arXiv.2102.05996. short: N.H. Konstantinov, C. Lampert, ArXiv (n.d.). date_created: 2022-02-28T14:13:59Z date_published: 2021-06-07T00:00:00Z date_updated: 2023-09-07T13:42:08Z day: '07' department: - _id: ChLa doi: 10.48550/arXiv.2102.05996 external_id: arxiv: - '2102.05996' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2102.05996 month: '06' oa: 1 oa_version: Preprint publication: arXiv publication_status: submitted related_material: record: - id: '10799' relation: dissertation_contains status: public status: public title: Fairness through regularization for learning to rank type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '9418' abstract: - lang: eng text: "Deep learning is best known for its empirical success across a wide range of applications\r\nspanning computer vision, natural language processing and speech. Of equal significance,\r\nthough perhaps less known, are its ramifications for learning theory: deep networks have\r\nbeen observed to perform surprisingly well in the high-capacity regime, aka the overfitting\r\nor underspecified regime. Classically, this regime on the far right of the bias-variance curve\r\nis associated with poor generalisation; however, recent experiments with deep networks\r\nchallenge this view.\r\n\r\nThis thesis is devoted to investigating various aspects of underspecification in deep learning.\r\nFirst, we argue that deep learning models are underspecified on two levels: a) any given\r\ntraining dataset can be fit by many different functions, and b) any given function can be\r\nexpressed by many different parameter configurations. We refer to the second kind of\r\nunderspecification as parameterisation redundancy and we precisely characterise its extent.\r\nSecond, we characterise the implicit criteria (the inductive bias) that guide learning in the\r\nunderspecified regime. Specifically, we consider a nonlinear but tractable classification\r\nsetting, and show that given the choice, neural networks learn classifiers with a large margin.\r\nThird, we consider learning scenarios where the inductive bias is not by itself sufficient to\r\ndeal with underspecification. We then study different ways of ‘tightening the specification’: i)\r\nIn the setting of representation learning with variational autoencoders, we propose a hand-\r\ncrafted regulariser based on mutual information. ii) In the setting of binary classification, we\r\nconsider soft-label (real-valued) supervision. We derive a generalisation bound for linear\r\nnetworks supervised in this way and verify that soft labels facilitate fast learning. Finally, we\r\nexplore an application of soft-label supervision to the training of multi-exit models." acknowledged_ssus: - _id: ScienComp - _id: CampIT - _id: E-Lib alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Phuong full_name: Bui Thi Mai, Phuong id: 3EC6EE64-F248-11E8-B48F-1D18A9856A87 last_name: Bui Thi Mai citation: ama: Phuong M. Underspecification in deep learning. 2021. doi:10.15479/AT:ISTA:9418 apa: Phuong, M. (2021). Underspecification in deep learning. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:9418 chicago: Phuong, Mary. “Underspecification in Deep Learning.” Institute of Science and Technology Austria, 2021. https://doi.org/10.15479/AT:ISTA:9418. ieee: M. Phuong, “Underspecification in deep learning,” Institute of Science and Technology Austria, 2021. ista: Phuong M. 2021. Underspecification in deep learning. Institute of Science and Technology Austria. mla: Phuong, Mary. Underspecification in Deep Learning. Institute of Science and Technology Austria, 2021, doi:10.15479/AT:ISTA:9418. short: M. Phuong, Underspecification in Deep Learning, Institute of Science and Technology Austria, 2021. date_created: 2021-05-24T13:06:23Z date_published: 2021-05-30T00:00:00Z date_updated: 2023-09-08T11:11:12Z day: '30' ddc: - '000' degree_awarded: PhD department: - _id: GradSch - _id: ChLa doi: 10.15479/AT:ISTA:9418 file: - access_level: open_access checksum: 4f0abe64114cfed264f9d36e8d1197e3 content_type: application/pdf creator: bphuong date_created: 2021-05-24T11:22:29Z date_updated: 2021-05-24T11:22:29Z file_id: '9419' file_name: mph-thesis-v519-pdfimages.pdf file_size: 2673905 relation: main_file success: 1 - access_level: closed checksum: f5699e876bc770a9b0df8345a77720a2 content_type: application/zip creator: bphuong date_created: 2021-05-24T11:56:02Z date_updated: 2021-05-24T11:56:02Z file_id: '9420' file_name: thesis.zip file_size: 92995100 relation: source_file file_date_updated: 2021-05-24T11:56:02Z has_accepted_license: '1' language: - iso: eng month: '05' oa: 1 oa_version: Published Version page: '125' publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '7435' relation: part_of_dissertation status: deleted - id: '7481' relation: part_of_dissertation status: public - id: '9416' relation: part_of_dissertation status: public - id: '7479' relation: part_of_dissertation status: public status: public supervisor: - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 title: Underspecification in deep learning type: dissertation user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2021' ... --- _id: '14987' abstract: - lang: eng text: "The goal of zero-shot learning is to construct a classifier that can identify object classes for which no training examples are available. When training data for some of the object classes is available but not for others, the name generalized zero-shot learning is commonly used.\r\nIn a wider sense, the phrase zero-shot is also used to describe other machine learning-based approaches that require no training data from the problem of interest, such as zero-shot action recognition or zero-shot machine translation." article_processing_charge: No author: - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Lampert C. Zero-Shot Learning. In: Ikeuchi K, ed. Computer Vision. 2nd ed. Cham: Springer; 2021:1395-1397. doi:10.1007/978-3-030-63416-2_874' apa: 'Lampert, C. (2021). Zero-Shot Learning. In K. Ikeuchi (Ed.), Computer Vision (2nd ed., pp. 1395–1397). Cham: Springer. https://doi.org/10.1007/978-3-030-63416-2_874' chicago: 'Lampert, Christoph. “Zero-Shot Learning.” In Computer Vision, edited by Katsushi Ikeuchi, 2nd ed., 1395–97. Cham: Springer, 2021. https://doi.org/10.1007/978-3-030-63416-2_874.' ieee: 'C. Lampert, “Zero-Shot Learning,” in Computer Vision, 2nd ed., K. Ikeuchi, Ed. Cham: Springer, 2021, pp. 1395–1397.' ista: 'Lampert C. 2021.Zero-Shot Learning. In: Computer Vision. , 1395–1397.' mla: Lampert, Christoph. “Zero-Shot Learning.” Computer Vision, edited by Katsushi Ikeuchi, 2nd ed., Springer, 2021, pp. 1395–97, doi:10.1007/978-3-030-63416-2_874. short: C. Lampert, in:, K. Ikeuchi (Ed.), Computer Vision, 2nd ed., Springer, Cham, 2021, pp. 1395–1397. date_created: 2024-02-14T14:05:32Z date_published: 2021-10-13T00:00:00Z date_updated: 2024-02-19T10:59:04Z day: '13' department: - _id: ChLa doi: 10.1007/978-3-030-63416-2_874 edition: '2' editor: - first_name: Katsushi full_name: Ikeuchi, Katsushi last_name: Ikeuchi language: - iso: eng month: '10' oa_version: None page: 1395-1397 place: Cham publication: Computer Vision publication_identifier: eisbn: - '9783030634162' isbn: - '9783030634155' publication_status: published publisher: Springer quality_controlled: '1' status: public title: Zero-Shot Learning type: book_chapter user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '8063' abstract: - lang: eng text: "We present a generative model of images that explicitly reasons over the set\r\nof objects they show. Our model learns a structured latent representation that\r\nseparates objects from each other and from the background; unlike prior works,\r\nit explicitly represents the 2D position and depth of each object, as well as\r\nan embedding of its segmentation mask and appearance. The model can be trained\r\nfrom images alone in a purely unsupervised fashion without the need for object\r\nmasks or depth information. Moreover, it always generates complete objects,\r\neven though a significant fraction of training images contain occlusions.\r\nFinally, we show that our model can infer decompositions of novel images into\r\ntheir constituent objects, including accurate prediction of depth ordering and\r\nsegmentation of occluded parts." article_number: '2004.00642' article_processing_charge: No author: - first_name: Titas full_name: Anciukevicius, Titas last_name: Anciukevicius - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Paul M full_name: Henderson, Paul M id: 13C09E74-18D9-11E9-8878-32CFE5697425 last_name: Henderson orcid: 0000-0002-5198-7445 citation: ama: Anciukevicius T, Lampert C, Henderson PM. Object-centric image generation with factored depths, locations, and appearances. arXiv. apa: Anciukevicius, T., Lampert, C., & Henderson, P. M. (n.d.). Object-centric image generation with factored depths, locations, and appearances. arXiv. chicago: Anciukevicius, Titas, Christoph Lampert, and Paul M Henderson. “Object-Centric Image Generation with Factored Depths, Locations, and Appearances.” ArXiv, n.d. ieee: T. Anciukevicius, C. Lampert, and P. M. Henderson, “Object-centric image generation with factored depths, locations, and appearances,” arXiv. . ista: Anciukevicius T, Lampert C, Henderson PM. Object-centric image generation with factored depths, locations, and appearances. arXiv, 2004.00642. mla: Anciukevicius, Titas, et al. “Object-Centric Image Generation with Factored Depths, Locations, and Appearances.” ArXiv, 2004.00642. short: T. Anciukevicius, C. Lampert, P.M. Henderson, ArXiv (n.d.). date_created: 2020-06-29T23:55:23Z date_published: 2020-04-01T00:00:00Z date_updated: 2021-01-12T08:16:44Z day: '01' ddc: - '004' department: - _id: ChLa external_id: arxiv: - '2004.00642' language: - iso: eng license: https://creativecommons.org/licenses/by-sa/4.0/ main_file_link: - open_access: '1' url: https://arxiv.org/abs/2004.00642 month: '04' oa: 1 oa_version: Preprint publication: arXiv publication_status: submitted status: public title: Object-centric image generation with factored depths, locations, and appearances tmp: image: /images/cc_by_sa.png legal_code_url: https://creativecommons.org/licenses/by-sa/4.0/legalcode name: Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0) short: CC BY-SA (4.0) type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2020' ... --- _id: '8188' abstract: - lang: eng text: "A natural approach to generative modeling of videos is to represent them as a composition of moving objects. Recent works model a set of 2D sprites over a slowly-varying background, but without considering the underlying 3D scene that\r\ngives rise to them. We instead propose to model a video as the view seen while moving through a scene with multiple 3D objects and a 3D background. Our model is trained from monocular videos without any supervision, yet learns to\r\ngenerate coherent 3D scenes containing several moving objects. We conduct detailed experiments on two datasets, going beyond the visual complexity supported by state-of-the-art generative approaches. We evaluate our method on\r\ndepth-prediction and 3D object detection---tasks which cannot be addressed by those earlier works---and show it out-performs them even on 2D instance segmentation and tracking." acknowledged_ssus: - _id: ScienComp acknowledgement: "This research was supported by the Scientific Service Units (SSU) of IST Austria through resources\r\nprovided by Scientific Computing (SciComp). PH is employed part-time by Blackford Analysis, but\r\nthey did not support this project in any way." article_processing_charge: No author: - first_name: Paul M full_name: Henderson, Paul M id: 13C09E74-18D9-11E9-8878-32CFE5697425 last_name: Henderson orcid: 0000-0002-5198-7445 - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Henderson PM, Lampert C. Unsupervised object-centric video generation and decomposition in 3D. In: 34th Conference on Neural Information Processing Systems. Vol 33. Curran Associates; 2020:3106–3117.' apa: 'Henderson, P. M., & Lampert, C. (2020). Unsupervised object-centric video generation and decomposition in 3D. In 34th Conference on Neural Information Processing Systems (Vol. 33, pp. 3106–3117). Vancouver, Canada: Curran Associates.' chicago: Henderson, Paul M, and Christoph Lampert. “Unsupervised Object-Centric Video Generation and Decomposition in 3D.” In 34th Conference on Neural Information Processing Systems, 33:3106–3117. Curran Associates, 2020. ieee: P. M. Henderson and C. Lampert, “Unsupervised object-centric video generation and decomposition in 3D,” in 34th Conference on Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 3106–3117. ista: 'Henderson PM, Lampert C. 2020. Unsupervised object-centric video generation and decomposition in 3D. 34th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 33, 3106–3117.' mla: Henderson, Paul M., and Christoph Lampert. “Unsupervised Object-Centric Video Generation and Decomposition in 3D.” 34th Conference on Neural Information Processing Systems, vol. 33, Curran Associates, 2020, pp. 3106–3117. short: P.M. Henderson, C. Lampert, in:, 34th Conference on Neural Information Processing Systems, Curran Associates, 2020, pp. 3106–3117. conference: end_date: 2020-12-12 location: Vancouver, Canada name: 'NeurIPS: Neural Information Processing Systems' start_date: 2020-12-06 date_created: 2020-07-31T16:59:19Z date_published: 2020-07-07T00:00:00Z date_updated: 2023-04-25T09:49:58Z day: '07' department: - _id: ChLa external_id: arxiv: - '2007.06705' intvolume: ' 33' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2007.06705 month: '07' oa: 1 oa_version: Preprint page: 3106–3117 publication: 34th Conference on Neural Information Processing Systems publication_identifier: isbn: - '9781713829546' publication_status: published publisher: Curran Associates quality_controlled: '1' status: public title: Unsupervised object-centric video generation and decomposition in 3D type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 33 year: '2020' ... --- _id: '6952' abstract: - lang: eng text: 'We present a unified framework tackling two problems: class-specific 3D reconstruction from a single image, and generation of new 3D shape samples. These tasks have received considerable attention recently; however, most existing approaches rely on 3D supervision, annotation of 2D images with keypoints or poses, and/or training with multiple views of each object instance. Our framework is very general: it can be trained in similar settings to existing approaches, while also supporting weaker supervision. Importantly, it can be trained purely from 2D images, without pose annotations, and with only a single view per instance. We employ meshes as an output representation, instead of voxels used in most prior work. This allows us to reason over lighting parameters and exploit shading information during training, which previous 2D-supervised methods cannot. Thus, our method can learn to generate and reconstruct concave object classes. We evaluate our approach in various settings, showing that: (i) it learns to disentangle shape from pose and lighting; (ii) using shading in the loss improves performance compared to just silhouettes; (iii) when using a standard single white light, our model outperforms state-of-the-art 2D-supervised methods, both with and without pose supervision, thanks to exploiting shading cues; (iv) performance improves further when using multiple coloured lights, even approaching that of state-of-the-art 3D-supervised methods; (v) shapes produced by our model capture smooth surfaces and fine details better than voxel-based approaches; and (vi) our approach supports concave classes such as bathtubs and sofas, which methods based on silhouettes cannot learn.' acknowledgement: Open access funding provided by Institute of Science and Technology (IST Austria). article_processing_charge: Yes (via OA deal) article_type: original author: - first_name: Paul M full_name: Henderson, Paul M id: 13C09E74-18D9-11E9-8878-32CFE5697425 last_name: Henderson orcid: 0000-0002-5198-7445 - first_name: Vittorio full_name: Ferrari, Vittorio last_name: Ferrari citation: ama: Henderson PM, Ferrari V. Learning single-image 3D reconstruction by generative modelling of shape, pose and shading. International Journal of Computer Vision. 2020;128:835-854. doi:10.1007/s11263-019-01219-8 apa: Henderson, P. M., & Ferrari, V. (2020). Learning single-image 3D reconstruction by generative modelling of shape, pose and shading. International Journal of Computer Vision. Springer Nature. https://doi.org/10.1007/s11263-019-01219-8 chicago: Henderson, Paul M, and Vittorio Ferrari. “Learning Single-Image 3D Reconstruction by Generative Modelling of Shape, Pose and Shading.” International Journal of Computer Vision. Springer Nature, 2020. https://doi.org/10.1007/s11263-019-01219-8. ieee: P. M. Henderson and V. Ferrari, “Learning single-image 3D reconstruction by generative modelling of shape, pose and shading,” International Journal of Computer Vision, vol. 128. Springer Nature, pp. 835–854, 2020. ista: Henderson PM, Ferrari V. 2020. Learning single-image 3D reconstruction by generative modelling of shape, pose and shading. International Journal of Computer Vision. 128, 835–854. mla: Henderson, Paul M., and Vittorio Ferrari. “Learning Single-Image 3D Reconstruction by Generative Modelling of Shape, Pose and Shading.” International Journal of Computer Vision, vol. 128, Springer Nature, 2020, pp. 835–54, doi:10.1007/s11263-019-01219-8. short: P.M. Henderson, V. Ferrari, International Journal of Computer Vision 128 (2020) 835–854. date_created: 2019-10-17T13:38:20Z date_published: 2020-04-01T00:00:00Z date_updated: 2023-08-17T14:01:16Z day: '01' ddc: - '004' department: - _id: ChLa doi: 10.1007/s11263-019-01219-8 external_id: arxiv: - '1901.06447' isi: - '000491042100002' file: - access_level: open_access checksum: a0f05dd4f5f64e4f713d8d9d4b5b1e3f content_type: application/pdf creator: dernst date_created: 2019-10-25T10:28:29Z date_updated: 2020-07-14T12:47:46Z file_id: '6973' file_name: 2019_CompVision_Henderson.pdf file_size: 2243134 relation: main_file file_date_updated: 2020-07-14T12:47:46Z has_accepted_license: '1' intvolume: ' 128' isi: 1 language: - iso: eng month: '04' oa: 1 oa_version: Published Version page: 835-854 project: - _id: B67AFEDC-15C9-11EA-A837-991A96BB2854 name: IST Austria Open Access Fund publication: International Journal of Computer Vision publication_identifier: eissn: - 1573-1405 issn: - 0920-5691 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Learning single-image 3D reconstruction by generative modelling of shape, pose and shading tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 128 year: '2020' ... --- _id: '7936' abstract: - lang: eng text: 'State-of-the-art detection systems are generally evaluated on their ability to exhaustively retrieve objects densely distributed in the image, across a wide variety of appearances and semantic categories. Orthogonal to this, many real-life object detection applications, for example in remote sensing, instead require dealing with large images that contain only a few small objects of a single class, scattered heterogeneously across the space. In addition, they are often subject to strict computational constraints, such as limited battery capacity and computing power.To tackle these more practical scenarios, we propose a novel flexible detection scheme that efficiently adapts to variable object sizes and densities: We rely on a sequence of detection stages, each of which has the ability to predict groups of objects as well as individuals. Similar to a detection cascade, this multi-stage architecture spares computational effort by discarding large irrelevant regions of the image early during the detection process. The ability to group objects provides further computational and memory savings, as it allows working with lower image resolutions in early stages, where groups are more easily detected than individuals, as they are more salient. We report experimental results on two aerial image datasets, and show that the proposed method is as accurate yet computationally more efficient than standard single-shot detectors, consistently across three different backbone architectures.' article_number: 1716-1725 article_processing_charge: No author: - first_name: Amélie full_name: Royer, Amélie id: 3811D890-F248-11E8-B48F-1D18A9856A87 last_name: Royer orcid: 0000-0002-8407-0705 - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Royer A, Lampert C. Localizing grouped instances for efficient detection in low-resource scenarios. In: IEEE Winter Conference on Applications of Computer Vision. IEEE; 2020. doi:10.1109/WACV45572.2020.9093288' apa: 'Royer, A., & Lampert, C. (2020). Localizing grouped instances for efficient detection in low-resource scenarios. In IEEE Winter Conference on Applications of Computer Vision. Snowmass Village, CO, United States: IEEE. https://doi.org/10.1109/WACV45572.2020.9093288' chicago: Royer, Amélie, and Christoph Lampert. “Localizing Grouped Instances for Efficient Detection in Low-Resource Scenarios.” In IEEE Winter Conference on Applications of Computer Vision. IEEE, 2020. https://doi.org/10.1109/WACV45572.2020.9093288. ieee: A. Royer and C. Lampert, “Localizing grouped instances for efficient detection in low-resource scenarios,” in IEEE Winter Conference on Applications of Computer Vision, Snowmass Village, CO, United States, 2020. ista: 'Royer A, Lampert C. 2020. Localizing grouped instances for efficient detection in low-resource scenarios. IEEE Winter Conference on Applications of Computer Vision. WACV: Winter Conference on Applications of Computer Vision, 1716–1725.' mla: Royer, Amélie, and Christoph Lampert. “Localizing Grouped Instances for Efficient Detection in Low-Resource Scenarios.” IEEE Winter Conference on Applications of Computer Vision, 1716–1725, IEEE, 2020, doi:10.1109/WACV45572.2020.9093288. short: A. Royer, C. Lampert, in:, IEEE Winter Conference on Applications of Computer Vision, IEEE, 2020. conference: end_date: 2020-03-05 location: ' Snowmass Village, CO, United States' name: 'WACV: Winter Conference on Applications of Computer Vision' start_date: 2020-03-01 date_created: 2020-06-07T22:00:53Z date_published: 2020-03-01T00:00:00Z date_updated: 2023-09-07T13:16:17Z day: '01' department: - _id: ChLa doi: 10.1109/WACV45572.2020.9093288 external_id: arxiv: - '2004.12623' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2004.12623 month: '03' oa: 1 oa_version: Preprint publication: IEEE Winter Conference on Applications of Computer Vision publication_identifier: isbn: - '9781728165530' publication_status: published publisher: IEEE quality_controlled: '1' related_material: record: - id: '8331' relation: dissertation_contains status: deleted - id: '8390' relation: dissertation_contains status: public scopus_import: 1 status: public title: Localizing grouped instances for efficient detection in low-resource scenarios type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2020' ... --- _id: '7937' abstract: - lang: eng text: 'Fine-tuning is a popular way of exploiting knowledge contained in a pre-trained convolutional network for a new visual recognition task. However, the orthogonal setting of transferring knowledge from a pretrained network to a visually different yet semantically close source is rarely considered: This commonly happens with real-life data, which is not necessarily as clean as the training source (noise, geometric transformations, different modalities, etc.).To tackle such scenarios, we introduce a new, generalized form of fine-tuning, called flex-tuning, in which any individual unit (e.g. layer) of a network can be tuned, and the most promising one is chosen automatically. In order to make the method appealing for practical use, we propose two lightweight and faster selection procedures that prove to be good approximations in practice. We study these selection criteria empirically across a variety of domain shifts and data scarcity scenarios, and show that fine-tuning individual units, despite its simplicity, yields very good results as an adaptation technique. As it turns out, in contrast to common practice, rather than the last fully-connected unit it is best to tune an intermediate or early one in many domain- shift scenarios, which is accurately detected by flex-tuning.' article_number: 2180-2189 article_processing_charge: No author: - first_name: Amélie full_name: Royer, Amélie id: 3811D890-F248-11E8-B48F-1D18A9856A87 last_name: Royer orcid: 0000-0002-8407-0705 - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Royer A, Lampert C. A flexible selection scheme for minimum-effort transfer learning. In: 2020 IEEE Winter Conference on Applications of Computer Vision. IEEE; 2020. doi:10.1109/WACV45572.2020.9093635' apa: 'Royer, A., & Lampert, C. (2020). A flexible selection scheme for minimum-effort transfer learning. In 2020 IEEE Winter Conference on Applications of Computer Vision. Snowmass Village, CO, United States: IEEE. https://doi.org/10.1109/WACV45572.2020.9093635' chicago: Royer, Amélie, and Christoph Lampert. “A Flexible Selection Scheme for Minimum-Effort Transfer Learning.” In 2020 IEEE Winter Conference on Applications of Computer Vision. IEEE, 2020. https://doi.org/10.1109/WACV45572.2020.9093635. ieee: A. Royer and C. Lampert, “A flexible selection scheme for minimum-effort transfer learning,” in 2020 IEEE Winter Conference on Applications of Computer Vision, Snowmass Village, CO, United States, 2020. ista: 'Royer A, Lampert C. 2020. A flexible selection scheme for minimum-effort transfer learning. 2020 IEEE Winter Conference on Applications of Computer Vision. WACV: Winter Conference on Applications of Computer Vision, 2180–2189.' mla: Royer, Amélie, and Christoph Lampert. “A Flexible Selection Scheme for Minimum-Effort Transfer Learning.” 2020 IEEE Winter Conference on Applications of Computer Vision, 2180–2189, IEEE, 2020, doi:10.1109/WACV45572.2020.9093635. short: A. Royer, C. Lampert, in:, 2020 IEEE Winter Conference on Applications of Computer Vision, IEEE, 2020. conference: end_date: 2020-03-05 location: Snowmass Village, CO, United States name: 'WACV: Winter Conference on Applications of Computer Vision' start_date: 2020-03-01 date_created: 2020-06-07T22:00:53Z date_published: 2020-03-01T00:00:00Z date_updated: 2023-09-07T13:16:17Z day: '01' department: - _id: ChLa doi: 10.1109/WACV45572.2020.9093635 external_id: arxiv: - '2008.11995' language: - iso: eng main_file_link: - open_access: '1' url: http://arxiv.org/abs/2008.11995 month: '03' oa: 1 oa_version: Preprint publication: 2020 IEEE Winter Conference on Applications of Computer Vision publication_identifier: isbn: - '9781728165530' publication_status: published publisher: IEEE quality_controlled: '1' related_material: record: - id: '8331' relation: dissertation_contains status: deleted - id: '8390' relation: dissertation_contains status: public scopus_import: '1' status: public title: A flexible selection scheme for minimum-effort transfer learning type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2020' ... --- _id: '8092' abstract: - lang: eng text: Image translation refers to the task of mapping images from a visual domain to another. Given two unpaired collections of images, we aim to learn a mapping between the corpus-level style of each collection, while preserving semantic content shared across the two domains. We introduce xgan, a dual adversarial auto-encoder, which captures a shared representation of the common domain semantic content in an unsupervised way, while jointly learning the domain-to-domain image translations in both directions. We exploit ideas from the domain adaptation literature and define a semantic consistency loss which encourages the learned embedding to preserve semantics shared across domains. We report promising qualitative results for the task of face-to-cartoon translation. The cartoon dataset we collected for this purpose, “CartoonSet”, is also publicly available as a new benchmark for semantic style transfer at https://google.github.io/cartoonset/index.html. article_processing_charge: No author: - first_name: Amélie full_name: Royer, Amélie id: 3811D890-F248-11E8-B48F-1D18A9856A87 last_name: Royer orcid: 0000-0002-8407-0705 - first_name: Konstantinos full_name: Bousmalis, Konstantinos last_name: Bousmalis - first_name: Stephan full_name: Gouws, Stephan last_name: Gouws - first_name: Fred full_name: Bertsch, Fred last_name: Bertsch - first_name: Inbar full_name: Mosseri, Inbar last_name: Mosseri - first_name: Forrester full_name: Cole, Forrester last_name: Cole - first_name: Kevin full_name: Murphy, Kevin last_name: Murphy citation: ama: 'Royer A, Bousmalis K, Gouws S, et al. XGAN: Unsupervised image-to-image translation for many-to-many mappings. In: Singh R, Vatsa M, Patel VM, Ratha N, eds. Domain Adaptation for Visual Understanding. Springer Nature; 2020:33-49. doi:10.1007/978-3-030-30671-7_3' apa: 'Royer, A., Bousmalis, K., Gouws, S., Bertsch, F., Mosseri, I., Cole, F., & Murphy, K. (2020). XGAN: Unsupervised image-to-image translation for many-to-many mappings. In R. Singh, M. Vatsa, V. M. Patel, & N. Ratha (Eds.), Domain Adaptation for Visual Understanding (pp. 33–49). Springer Nature. https://doi.org/10.1007/978-3-030-30671-7_3' chicago: 'Royer, Amélie, Konstantinos Bousmalis, Stephan Gouws, Fred Bertsch, Inbar Mosseri, Forrester Cole, and Kevin Murphy. “XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings.” In Domain Adaptation for Visual Understanding, edited by Richa Singh, Mayank Vatsa, Vishal M. Patel, and Nalini Ratha, 33–49. Springer Nature, 2020. https://doi.org/10.1007/978-3-030-30671-7_3.' ieee: 'A. Royer et al., “XGAN: Unsupervised image-to-image translation for many-to-many mappings,” in Domain Adaptation for Visual Understanding, R. Singh, M. Vatsa, V. M. Patel, and N. Ratha, Eds. Springer Nature, 2020, pp. 33–49.' ista: 'Royer A, Bousmalis K, Gouws S, Bertsch F, Mosseri I, Cole F, Murphy K. 2020.XGAN: Unsupervised image-to-image translation for many-to-many mappings. In: Domain Adaptation for Visual Understanding. , 33–49.' mla: 'Royer, Amélie, et al. “XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings.” Domain Adaptation for Visual Understanding, edited by Richa Singh et al., Springer Nature, 2020, pp. 33–49, doi:10.1007/978-3-030-30671-7_3.' short: A. Royer, K. Bousmalis, S. Gouws, F. Bertsch, I. Mosseri, F. Cole, K. Murphy, in:, R. Singh, M. Vatsa, V.M. Patel, N. Ratha (Eds.), Domain Adaptation for Visual Understanding, Springer Nature, 2020, pp. 33–49. date_created: 2020-07-05T22:00:46Z date_published: 2020-01-08T00:00:00Z date_updated: 2023-09-07T13:16:18Z day: '08' department: - _id: ChLa doi: 10.1007/978-3-030-30671-7_3 editor: - first_name: Richa full_name: Singh, Richa last_name: Singh - first_name: Mayank full_name: Vatsa, Mayank last_name: Vatsa - first_name: Vishal M. full_name: Patel, Vishal M. last_name: Patel - first_name: Nalini full_name: Ratha, Nalini last_name: Ratha external_id: arxiv: - '1711.05139' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1711.05139 month: '01' oa: 1 oa_version: Preprint page: 33-49 publication: Domain Adaptation for Visual Understanding publication_identifier: isbn: - '9783030306717' publication_status: published publisher: Springer Nature quality_controlled: '1' related_material: record: - id: '8331' relation: dissertation_contains status: deleted - id: '8390' relation: dissertation_contains status: public scopus_import: '1' status: public title: 'XGAN: Unsupervised image-to-image translation for many-to-many mappings' type: book_chapter user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2020' ... --- _id: '7481' abstract: - lang: eng text: 'We address the following question: How redundant is the parameterisation of ReLU networks? Specifically, we consider transformations of the weight space which leave the function implemented by the network intact. Two such transformations are known for feed-forward architectures: permutation of neurons within a layer, and positive scaling of all incoming weights of a neuron coupled with inverse scaling of its outgoing weights. In this work, we show for architectures with non-increasing widths that permutation and scaling are in fact the only function-preserving weight transformations. For any eligible architecture we give an explicit construction of a neural network such that any other network that implements the same function can be obtained from the original one by the application of permutations and rescaling. The proof relies on a geometric understanding of boundaries between linear regions of ReLU networks, and we hope the developed mathematical tools are of independent interest.' article_processing_charge: No author: - first_name: Phuong full_name: Bui Thi Mai, Phuong id: 3EC6EE64-F248-11E8-B48F-1D18A9856A87 last_name: Bui Thi Mai - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Phuong M, Lampert C. Functional vs. parametric equivalence of ReLU networks. In: 8th International Conference on Learning Representations. ; 2020.' apa: Phuong, M., & Lampert, C. (2020). Functional vs. parametric equivalence of ReLU networks. In 8th International Conference on Learning Representations. Online. chicago: Phuong, Mary, and Christoph Lampert. “Functional vs. Parametric Equivalence of ReLU Networks.” In 8th International Conference on Learning Representations, 2020. ieee: M. Phuong and C. Lampert, “Functional vs. parametric equivalence of ReLU networks,” in 8th International Conference on Learning Representations, Online, 2020. ista: 'Phuong M, Lampert C. 2020. Functional vs. parametric equivalence of ReLU networks. 8th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.' mla: Phuong, Mary, and Christoph Lampert. “Functional vs. Parametric Equivalence of ReLU Networks.” 8th International Conference on Learning Representations, 2020. short: M. Phuong, C. Lampert, in:, 8th International Conference on Learning Representations, 2020. conference: end_date: 2020-04-30 location: Online name: 'ICLR: International Conference on Learning Representations' start_date: 2020-04-27 date_created: 2020-02-11T09:07:37Z date_published: 2020-04-26T00:00:00Z date_updated: 2023-09-07T13:29:50Z day: '26' ddc: - '000' department: - _id: ChLa file: - access_level: open_access checksum: 8d372ea5defd8cb8fdc430111ed754a9 content_type: application/pdf creator: bphuong date_created: 2020-02-11T09:07:27Z date_updated: 2020-07-14T12:47:59Z file_id: '7482' file_name: main.pdf file_size: 405469 relation: main_file file_date_updated: 2020-07-14T12:47:59Z has_accepted_license: '1' language: - iso: eng month: '04' oa: 1 oa_version: Published Version publication: 8th International Conference on Learning Representations publication_status: published quality_controlled: '1' related_material: link: - relation: supplementary_material url: https://iclr.cc/virtual_2020/poster_Bylx-TNKvH.html record: - id: '9418' relation: dissertation_contains status: public status: public title: Functional vs. parametric equivalence of ReLU networks type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2020' ...