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Author  |
Adriana Romero; Petia Radeva; Carlo Gatta |
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Title |
No more meta-parameter tuning in unsupervised sparse feature learning |
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Miscellaneous |
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2014 |
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Arxiv |
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CoRR abs/1402.5766
We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on STL-10 show that the method presents state-of-the-art performance and provides discriminative features that generalize well. |
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MILAB; LAMP; 600.079 |
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Admin @ si @ RRG2014 |
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2471 |
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Author |
Adriana Romero; Petia Radeva; Carlo Gatta |
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Title |
Meta-parameter free unsupervised sparse feature learning |
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Journal Article |
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2015 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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37 |
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8 |
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1716-1722 |
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We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on CIFAR-10, STL- 10 and UCMerced show that the method achieves the state-of-theart performance, providing discriminative features that generalize well. |
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MILAB; 600.068; 600.079; 601.160 |
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Admin @ si @ RRG2014b |
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2594 |
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Author |
Adriana Romero; Simeon Petkov; Carlo Gatta; M.Sabate; Petia Radeva |
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Title |
Efficient automatic segmentation of vessels |
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2012 |
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16th Conference on Medical Image Understanding and Analysis |
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Swansea, United Kingdom |
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MIUA |
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MILAB |
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Admin @ si @ |
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2137 |
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Adrien Gaidon; Antonio Lopez; Florent Perronnin |
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Title |
The Reasonable Effectiveness of Synthetic Visual Data |
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2018 |
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International Journal of Computer Vision |
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IJCV |
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126 |
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9 |
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899–901 |
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ADAS; 600.118 |
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Admin @ si @ GLP2018 |
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3180 |
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Author |
Adrien Pavao; Isabelle Guyon; Anne-Catherine Letournel; Dinh-Tuan Tran; Xavier Baro; Hugo Jair Escalante; Sergio Escalera; Tyler Thomas; Zhen Xu |
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Title |
CodaLab Competitions: An Open Source Platform to Organize Scientific Challenges |
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2023 |
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Journal of Machine Learning Research |
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JMLR |
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CodaLab Competitions is an open source web platform designed to help data scientists and research teams to crowd-source the resolution of machine learning problems through the organization of competitions, also called challenges or contests. CodaLab Competitions provides useful features such as multiple phases, results and code submissions, multi-score leaderboards, and jobs running
inside Docker containers. The platform is very flexible and can handle large scale experiments, by allowing organizers to upload large datasets and provide their own CPU or GPU compute workers. |
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HUPBA |
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Admin @ si @ PGL2023 |
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3973 |
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Author |
Agata Lapedriza |
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Title |
Face Classification using External Face Features |
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Report |
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2005 |
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CVC Technical Report #83 |
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CVC (UAB) |
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OR;MV |
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BCNPCL @ bcnpcl @ Lap2005 |
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551 |
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Author |
Agata Lapedriza |
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Title |
Multitask Learning Techniques for Automatic Face Classification |
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2009 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Automatic face classification is currently a popular research area in Computer Vision. It involves several subproblems, such as subject recognition, gender classification or subject verification.
Current systems of automatic face classification need a large amount of training data to robustly learn a task. However, the collection of labeled data is usually a difficult issue. For this reason, the research on methods that are able to learn from a small sized training set is essential.
The dependency on the abundance of training data is not so evident in human learning processes. We are able to learn from a very small number of examples, given that we use, additionally, some prior knowledge to learn a new task. For example, we frequently find patterns and analogies from other domains to reuse them in new situations, or exploit training data from other experiences.
In computer science, Multitask Learning is a new Machine Learning approach that studies this idea of knowledge transfer among different tasks, to overcome the effects of the small sample sized problem.
This thesis explores, proposes and tests some Multitask Learning methods specially developed for face classification purposes. Moreover, it presents two more contributions dealing with the small sample sized problem, out of the Multitask Learning context. The first one is a method to extract external face features, to be used as an additional information source in automatic face classification problems. The second one is an empirical study on the most suitable face image resolution to perform automatic subject recognition. |
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Barcelona (Spain) |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Jordi Vitria;David Masip |
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BCNPCL @ bcnpcl @ Lap2009 |
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1263 |
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Author |
Agata Lapedriza; David Masip; D.Sanchez |
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Title |
Emotions Classification using Facial Action Units Recognition |
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2014 |
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17th International Conference of the Catalan Association for Artificial Intelligence |
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269 |
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55-64 |
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In this work we build a system for automatic emotion classification from image sequences. We analyze subtle changes in facial expressions by detecting a subset of 12 representative facial action units (AUs). Then, we classify emotions based on the output of these AUs classifiers, i.e. the presence/absence of AUs. We base the AUs classification upon a set of spatio-temporal geometric and appearance features for facial representation, fusing them within the emotion classifier. A decision tree is trained for emotion classifying, making the resulting model easy to interpret by capturing the combination of AUs activation that lead to a particular emotion. For Cohn-Kanade database, the proposed system classifies 7 emotions with a mean accuracy of near 90%, attaining a similar recognition accuracy in comparison with non-interpretable models that are not based in AUs detection. |
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978-1-61499-451-0 |
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CCIA |
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OR;MV |
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Admin @ si @ LMS2014 |
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2622 |
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Agata Lapedriza; David Masip; Jordi Vitria |
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The contribution of external features to face recognition |
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2005 |
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Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3523: 537–544 |
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Estoril (Portugal) |
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BCNPCL @ bcnpcl @ LMV2005a |
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546 |
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Agata Lapedriza; David Masip; Jordi Vitria |
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Are external face features useful for automatic face classification? |
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2005 |
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IEEE Workshop on Face Recognition Grand Challenge Experiments, 151–ff |
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San Diego; CA; USA; |
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BCNPCL @ bcnpcl @ LMV2005b |
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547 |
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