---
_id: '2189'
abstract:
- lang: fre
text: En apprentissage automatique, nous parlons d'adaptation de domaine lorsque
les données de test (cibles) et d'apprentissage (sources) sont générées selon
différentes distributions. Nous devons donc développer des algorithmes de classification
capables de s'adapter à une nouvelle distribution, pour laquelle aucune information
sur les étiquettes n'est disponible. Nous attaquons cette problématique sous l'angle
de l'approche PAC-Bayésienne qui se focalise sur l'apprentissage de modèles définis
comme des votes de majorité sur un ensemble de fonctions. Dans ce contexte, nous
introduisons PV-MinCq une version adaptative de l'algorithme (non adaptatif) MinCq.
PV-MinCq suit le principe suivant. Nous transférons les étiquettes sources aux
points cibles proches pour ensuite appliquer MinCq sur l'échantillon cible ``auto-étiqueté''
(justifié par une borne théorique). Plus précisément, nous définissons un auto-étiquetage
non itératif qui se focalise dans les régions où les distributions marginales
source et cible sont les plus similaires. Dans un second temps, nous étudions
l'influence de notre auto-étiquetage pour en déduire une procédure de validation
des hyperparamètres. Finalement, notre approche montre des résultats empiriques
prometteurs.
article_processing_charge: No
author:
- first_name: Emilie
full_name: Morvant, Emilie
id: 4BAC2A72-F248-11E8-B48F-1D18A9856A87
last_name: Morvant
orcid: 0000-0002-8301-7240
citation:
ama: 'Morvant E. Adaptation de domaine de vote de majorité par auto-étiquetage non
itératif. In: Vol 1. Elsevier; 2014:49-58.'
apa: 'Morvant, E. (2014). Adaptation de domaine de vote de majorité par auto-étiquetage
non itératif (Vol. 1, pp. 49–58). Presented at the CAP: Conférence Francophone
sur l’Apprentissage Automatique (Machine Learning French Conference), Saint-Etienne,
France: Elsevier.'
chicago: Morvant, Emilie. “Adaptation de Domaine de Vote de Majorité Par Auto-Étiquetage
Non Itératif,” 1:49–58. Elsevier, 2014.
ieee: 'E. Morvant, “Adaptation de domaine de vote de majorité par auto-étiquetage
non itératif,” presented at the CAP: Conférence Francophone sur l’Apprentissage
Automatique (Machine Learning French Conference), Saint-Etienne, France, 2014,
vol. 1, pp. 49–58.'
ista: 'Morvant E. 2014. Adaptation de domaine de vote de majorité par auto-étiquetage
non itératif. CAP: Conférence Francophone sur l’Apprentissage Automatique (Machine
Learning French Conference) vol. 1, 49–58.'
mla: Morvant, Emilie. Adaptation de Domaine de Vote de Majorité Par Auto-Étiquetage
Non Itératif. Vol. 1, Elsevier, 2014, pp. 49–58.
short: E. Morvant, in:, Elsevier, 2014, pp. 49–58.
conference:
location: Saint-Etienne, France
name: 'CAP: Conférence Francophone sur l''Apprentissage Automatique (Machine Learning
French Conference)'
date_created: 2018-12-11T11:56:13Z
date_published: 2014-07-01T00:00:00Z
date_updated: 2021-01-12T06:55:52Z
day: '01'
department:
- _id: ChLa
intvolume: ' 1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://hal.archives-ouvertes.fr/hal-01005776/
month: '07'
oa: 1
oa_version: Preprint
page: 49-58
publication_status: published
publisher: Elsevier
publist_id: '4785'
quality_controlled: '1'
status: public
title: Adaptation de domaine de vote de majorité par auto-étiquetage non itératif
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 1
year: '2014'
...
---
_id: '2160'
abstract:
- lang: eng
text: Transfer learning has received a lot of attention in the machine learning
community over the last years, and several effective algorithms have been developed.
However, relatively little is known about their theoretical properties, especially
in the setting of lifelong learning, where the goal is to transfer information
to tasks for which no data have been observed so far. In this work we study lifelong
learning from a theoretical perspective. Our main result is a PAC-Bayesian generalization
bound that offers a unified view on existing paradigms for transfer learning,
such as the transfer of parameters or the transfer of low-dimensional representations.
We also use the bound to derive two principled lifelong learning algorithms, and
we show that these yield results comparable with existing methods.
article_processing_charge: No
author:
- first_name: Anastasia
full_name: Pentina, Anastasia
id: 42E87FC6-F248-11E8-B48F-1D18A9856A87
last_name: Pentina
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Pentina A, Lampert C. A PAC-Bayesian bound for Lifelong Learning. In: Vol
32. ML Research Press; 2014:991-999.'
apa: 'Pentina, A., & Lampert, C. (2014). A PAC-Bayesian bound for Lifelong Learning
(Vol. 32, pp. 991–999). Presented at the ICML: International Conference on Machine
Learning, Beijing, China: ML Research Press.'
chicago: Pentina, Anastasia, and Christoph Lampert. “A PAC-Bayesian Bound for Lifelong
Learning,” 32:991–99. ML Research Press, 2014.
ieee: 'A. Pentina and C. Lampert, “A PAC-Bayesian bound for Lifelong Learning,”
presented at the ICML: International Conference on Machine Learning, Beijing,
China, 2014, vol. 32, pp. 991–999.'
ista: 'Pentina A, Lampert C. 2014. A PAC-Bayesian bound for Lifelong Learning. ICML:
International Conference on Machine Learning vol. 32, 991–999.'
mla: Pentina, Anastasia, and Christoph Lampert. A PAC-Bayesian Bound for Lifelong
Learning. Vol. 32, ML Research Press, 2014, pp. 991–99.
short: A. Pentina, C. Lampert, in:, ML Research Press, 2014, pp. 991–999.
conference:
end_date: 2014-06-26
location: Beijing, China
name: 'ICML: International Conference on Machine Learning'
start_date: 2014-06-21
date_created: 2018-12-11T11:56:03Z
date_published: 2014-05-10T00:00:00Z
date_updated: 2023-10-17T11:54:24Z
day: '10'
department:
- _id: ChLa
intvolume: ' 32'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://dl.acm.org/citation.cfm?id=3045003
month: '05'
oa: 1
oa_version: Submitted Version
page: 991 - 999
publication_status: published
publisher: ML Research Press
publist_id: '4844'
quality_controlled: '1'
scopus_import: '1'
status: public
title: A PAC-Bayesian bound for Lifelong Learning
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 32
year: '2014'
...
---
_id: '2294'
abstract:
- lang: eng
text: "In this work we propose a system for automatic classification of Drosophila
embryos into developmental stages.\r\nWhile the system is designed to solve an
actual problem in biological research, we believe that the principle underly-\r\ning
it is interesting not only for biologists, but also for researchers in computer
vision. The main idea is to combine two orthogonal sources of information: one
is a classifier trained on strongly invariant features, which makes it applicable
to images of very different conditions, but also leads to rather noisy predictions.
The other is a label propagation step based on a more powerful similarity measure
that however is only consistent within specific subsets of the data at a time.\r\nIn
our biological setup, the information sources are the shape and the staining patterns
of embryo images. We show\r\nexperimentally that while neither of the methods
\ can be used by itself to achieve satisfactory results, their combina-\r\ntion
achieves prediction quality comparable to human performance."
author:
- first_name: Tomas
full_name: Kazmar, Tomas
last_name: Kazmar
- first_name: Evgeny
full_name: Kvon, Evgeny
last_name: Kvon
- first_name: Alexander
full_name: Stark, Alexander
last_name: Stark
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Kazmar T, Kvon E, Stark A, Lampert C. Drosophila Embryo Stage Annotation using
Label Propagation. In: IEEE; 2013. doi:10.1109/ICCV.2013.139'
apa: 'Kazmar, T., Kvon, E., Stark, A., & Lampert, C. (2013). Drosophila Embryo
Stage Annotation using Label Propagation. Presented at the ICCV: International
Conference on Computer Vision, Sydney, Australia: IEEE. https://doi.org/10.1109/ICCV.2013.139'
chicago: Kazmar, Tomas, Evgeny Kvon, Alexander Stark, and Christoph Lampert. “Drosophila
Embryo Stage Annotation Using Label Propagation.” IEEE, 2013. https://doi.org/10.1109/ICCV.2013.139.
ieee: 'T. Kazmar, E. Kvon, A. Stark, and C. Lampert, “Drosophila Embryo Stage Annotation
using Label Propagation,” presented at the ICCV: International Conference on Computer
Vision, Sydney, Australia, 2013.'
ista: 'Kazmar T, Kvon E, Stark A, Lampert C. 2013. Drosophila Embryo Stage Annotation
using Label Propagation. ICCV: International Conference on Computer Vision.'
mla: Kazmar, Tomas, et al. Drosophila Embryo Stage Annotation Using Label Propagation.
IEEE, 2013, doi:10.1109/ICCV.2013.139.
short: T. Kazmar, E. Kvon, A. Stark, C. Lampert, in:, IEEE, 2013.
conference:
end_date: 2013-12-08
location: Sydney, Australia
name: 'ICCV: International Conference on Computer Vision'
start_date: 2013-12-01
date_created: 2018-12-11T11:56:49Z
date_published: 2013-12-01T00:00:00Z
date_updated: 2021-01-12T06:56:35Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/ICCV.2013.139
ec_funded: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.cv-foundation.org/openaccess/ICCV2013.py
month: '12'
oa: 1
oa_version: Submitted Version
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_status: published
publisher: IEEE
publist_id: '4634'
quality_controlled: '1'
scopus_import: 1
status: public
title: Drosophila Embryo Stage Annotation using Label Propagation
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2013'
...
---
_id: '2293'
abstract:
- lang: eng
text: Many computer vision problems have an asymmetric distribution of information
between training and test time. In this work, we study the case where we are given
additional information about the training data, which however will not be available
at test time. This situation is called learning using privileged information (LUPI).
We introduce two maximum-margin techniques that are able to make use of this additional
source of information, and we show that the framework is applicable to several
scenarios that have been studied in computer vision before. Experiments with attributes,
bounding boxes, image tags and rationales as additional information in object
classification show promising results.
author:
- first_name: Viktoriia
full_name: Sharmanska, Viktoriia
id: 2EA6D09E-F248-11E8-B48F-1D18A9856A87
last_name: Sharmanska
orcid: 0000-0003-0192-9308
- first_name: Novi
full_name: Quadrianto, Novi
last_name: Quadrianto
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Sharmanska V, Quadrianto N, Lampert C. Learning to rank using privileged information.
In: IEEE; 2013:825-832. doi:10.1109/ICCV.2013.107'
apa: 'Sharmanska, V., Quadrianto, N., & Lampert, C. (2013). Learning to rank
using privileged information (pp. 825–832). Presented at the ICCV: International
Conference on Computer Vision, Sydney, Australia: IEEE. https://doi.org/10.1109/ICCV.2013.107'
chicago: Sharmanska, Viktoriia, Novi Quadrianto, and Christoph Lampert. “Learning
to Rank Using Privileged Information,” 825–32. IEEE, 2013. https://doi.org/10.1109/ICCV.2013.107.
ieee: 'V. Sharmanska, N. Quadrianto, and C. Lampert, “Learning to rank using privileged
information,” presented at the ICCV: International Conference on Computer Vision,
Sydney, Australia, 2013, pp. 825–832.'
ista: 'Sharmanska V, Quadrianto N, Lampert C. 2013. Learning to rank using privileged
information. ICCV: International Conference on Computer Vision, 825–832.'
mla: Sharmanska, Viktoriia, et al. Learning to Rank Using Privileged Information.
IEEE, 2013, pp. 825–32, doi:10.1109/ICCV.2013.107.
short: V. Sharmanska, N. Quadrianto, C. Lampert, in:, IEEE, 2013, pp. 825–832.
conference:
end_date: 2013-12-08
location: Sydney, Australia
name: 'ICCV: International Conference on Computer Vision'
start_date: 2013-12-01
date_created: 2018-12-11T11:56:49Z
date_published: 2013-12-01T00:00:00Z
date_updated: 2023-02-23T10:36:41Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/ICCV.2013.107
ec_funded: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: www.cv-foundation.org/openaccess/content_iccv_2013/papers/Sharmanska_Learning_to_Rank_2013_ICCV_paper.pdf
month: '12'
oa: 1
oa_version: Submitted Version
page: 825 - 832
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_status: published
publisher: IEEE
publist_id: '4635'
quality_controlled: '1'
scopus_import: 1
status: public
title: Learning to rank using privileged information
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2013'
...
---
_id: '2516'
abstract:
- lang: eng
text: 'We study the problem of object recognition for categories for which we have
no training examples, a task also called zero-data or zero-shot learning. This
situation has hardly been studied in computer vision research, even though it
occurs frequently: the world contains tens of thousands of different object classes
and for only few of them image collections have been formed and suitably annotated.
To tackle the problem we introduce attribute-based classification: objects are
identified based on a high-level description that is phrased in terms of semantic
attributes, such as the object''s color or shape. Because the identification of
each such property transcends the specific learning task at hand, the attribute
classifiers can be pre-learned independently, e.g. from existing image datasets
unrelated to the current task. Afterwards, new classes can be detected based on
their attribute representation, without the need for a new training phase. In
this paper we also introduce a new dataset, Animals with Attributes, of over 30,000
images of 50 animal classes, annotated with 85 semantic attributes. Extensive
experiments on this and two more datasets show that attribute-based classification
indeed is able to categorize images without access to any training images of the
target classes.'
author:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Hannes
full_name: Nickisch, Hannes
last_name: Nickisch
- first_name: Stefan
full_name: Harmeling, Stefan
last_name: Harmeling
citation:
ama: Lampert C, Nickisch H, Harmeling S. Attribute-based classification for zero-shot
learning of object categories. IEEE Transactions on Pattern Analysis and Machine
Intelligence. 2013;36(3):453-465. doi:10.1109/TPAMI.2013.140
apa: Lampert, C., Nickisch, H., & Harmeling, S. (2013). Attribute-based classification
for zero-shot learning of object categories. IEEE Transactions on Pattern Analysis
and Machine Intelligence. IEEE. https://doi.org/10.1109/TPAMI.2013.140
chicago: Lampert, Christoph, Hannes Nickisch, and Stefan Harmeling. “Attribute-Based
Classification for Zero-Shot Learning of Object Categories.” IEEE Transactions
on Pattern Analysis and Machine Intelligence. IEEE, 2013. https://doi.org/10.1109/TPAMI.2013.140.
ieee: C. Lampert, H. Nickisch, and S. Harmeling, “Attribute-based classification
for zero-shot learning of object categories,” IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol. 36, no. 3. IEEE, pp. 453–465, 2013.
ista: Lampert C, Nickisch H, Harmeling S. 2013. Attribute-based classification for
zero-shot learning of object categories. IEEE Transactions on Pattern Analysis
and Machine Intelligence. 36(3), 453–465.
mla: Lampert, Christoph, et al. “Attribute-Based Classification for Zero-Shot Learning
of Object Categories.” IEEE Transactions on Pattern Analysis and Machine Intelligence,
vol. 36, no. 3, IEEE, 2013, pp. 453–65, doi:10.1109/TPAMI.2013.140.
short: C. Lampert, H. Nickisch, S. Harmeling, IEEE Transactions on Pattern Analysis
and Machine Intelligence 36 (2013) 453–465.
date_created: 2018-12-11T11:58:08Z
date_published: 2013-07-30T00:00:00Z
date_updated: 2021-01-12T06:57:58Z
day: '30'
department:
- _id: ChLa
doi: 10.1109/TPAMI.2013.140
intvolume: ' 36'
issue: '3'
language:
- iso: eng
month: '07'
oa_version: None
page: 453 - 465
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_status: published
publisher: IEEE
publist_id: '4385'
quality_controlled: '1'
scopus_import: 1
status: public
title: Attribute-based classification for zero-shot learning of object categories
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 36
year: '2013'
...