Challenges in representation learning: A report on three machine learning contests
Abstract
The ICML 2013 Workshop on Challenges in Representation Learning(1) focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge. We describe the datasets created for these challenges and summarize the results of the competitions. We provide suggestions for organizers of future challenges and some comments on what kind of knowledge can be gained from machine learning competitions.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565 · see citing works
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472 · see citing works
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653 · see citing works
Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436 · see citing works
Self-citationBradski, G. R. (2000) · Medical Entomology and Zoology · Cited by 5,994 · see citing works
Fukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759 · see citing works
Reading digits in natural images with unsupervised feature learning
Netzer, Y. (2024) · doi:10.57702/e7wwo0zk · Cited by 4,261 · see citing works
Labeling images with a computer game
von Ahn, L., & Dabbish, L. (2004) · ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) · doi:10.1145/985692.985733 · Cited by 2,230 · see citing works
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Cockton, G., & Korhonen, P. (2003) · doi:10.1145/642611 · Cited by 911 · see citing works
Deep Learning using Linear Support Vector MachinesPreprint
Tang, Y. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1306.0239 · Cited by 891 · see citing works
Self-citationTraining a Support Vector Machine in the Primal
Chapelle, O. (2007) · Neural Computation · doi:10.1162/neco.2007.19.5.1155 · Cited by 789 · see citing works
Pylearn2: a machine learning research libraryPreprint
Goodfellow, I., Warde-Farley, D., Lamblin, P., Dumoulin, V., Mirza, M., Pascanu, R., Bergstra, J., Bastien, F., & Bengio, Y. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1308.4214 · Cited by 245 · see citing works
Self-citationRepresentation Learning: A Review and New PerspectivesPreprint
Bengio, Y., Courville, A. C., & Vincent, P. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.5538 · Cited by 235 · see citing works
Self-citationNgiam, J., Chen, Z., Bhaskar, S. A., Koh, P. W., & Ng, A. Y. (2011) · neural information processing systems · Cited by 232 · see citing works
Large-Scale Feature Learning With Spike-and-Slab Sparse CodingPreprint
Goodfellow, I., Courville, A. C., & Bengio, Y. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.6407 · Cited by 49 · see citing works
Self-citationUnsupervised and transfer learning challenge
Guyon, I., Dror, G., Lemaire, V., Taylor, G. W., & Aha, D. W. (2011) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2011.6033302 · Cited by 37 · see citing works
Bergstra, J., & Cox, D. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1306.3476 · Cited by 16 · see citing works
Self-citationConstructing Hierarchical Image-tags Bimodal Representations for Word Tags Alternative ChoicePreprint
Feng, F., Li, R., & Wang, X. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1307.1275 · Cited by 11 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Deep Learning for Emotion Recognition on Small Datasets using Transfer Learning
Ng, H.-W., Nguyen, V. D., Vonikakis, V., & Winkler, S. (2015) · Proceedings of the 2015 ACM on International Conference on Multimodal Interaction · doi:10.1145/2818346.2830593 · Cited by 657 · see citing works
Text feature extraction based on deep learning: a review
Liang, H., Sun, X., Sun, Y., & Gao, Y. (2017) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-017-0993-1 · Cited by 373 · see citing works
Deep learning for human affect recognition: Insights and new developments
Rouast, P. V., Adam, M. T. P., & Chiong, R. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2890471 · Cited by 243 · see citing works
Video-based emotion recognition in the wild using deep transfer learning and score fusion
Kaya, H., Gürpınar, F., & Salah, A. A. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.01.012 · Cited by 235 · see citing works
Automated emotion recognition: Current trends and future perspectives
Maithri, M., Raghavendra, U., Gudigar, A., Samanth, J., Barua, P. D., Murugappan, M., Chakole, Y., & Acharya, U. R. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106646 · Cited by 172 · see citing works
Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges
Patel, K., Mehta, D., Mistry, C., Gupta, R., Tanwar, S., Kumar, N., & Alazab, M. (2020) · IEEE Access · doi:10.1109/access.2020.2993803 · Cited by 157 · see citing works
Facial Expression Recognition with Identity and Emotion Joint Learning
Li, M., Xu, H., Huang, X., Song, Z., Liu, X., & Li, X. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2880201 · Cited by 128 · see citing works
A multimodal emotion recognition method based on facial expressions and electroencephalography
Tan, Y., Sun, Z., Duan, F., Solé-Casals, J., & Caiafa, C. F. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103029 · Cited by 88 · see citing works
Kim, J., Kang, J.-K., & Kim, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3099075 · Cited by 50 · see citing works
Multimodal Attention Network for Continuous-Time Emotion Recognition Using Video and EEG Signals
Choi, D. Y., Kim, D.-H., & Song, B. C. (2020) · IEEE Access · doi:10.1109/access.2020.3036877 · Cited by 39 · see citing works
MindLink-Eumpy: An Open-Source Python Toolbox for Multimodal Emotion Recognition
Li, R., Liang, Y., Liu, X., Wang, B., Huang, W., Cai, Z., Ye, Y., Qiu, L., & Pan, J. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.621493 · Cited by 36 · see citing works
Self-citationFacial Expression Recognition System on SoC FPGA
Vinh, P. T., & Quang Vinh, T. (2019) · 2019 International Symposium on Electrical and Electronics Engineering (ISEE) · doi:10.1109/isee2.2019.8921140 · Cited by 22 · see citing works
Hierarchical fusion of visual and physiological signals for emotion recognition
Fang, Y., Rong, R., & Huang, J. (2021) · Multidimensional Systems and Signal Processing · doi:10.1007/s11045-021-00774-z · Cited by 17 · see citing works
- Authors
- Goodfellow, I. J., Erhan, D., Luc Carrier, P., Courville, A., Mirza, M., Hamner, B., Cukierski, W., Tang, Y., Thaler, D., Lee, D.-H., Zhou, Y., Ramaiah, C., Feng, F., Li, R., Wang, X., Athanasakis, D., Shawe-Taylor, J., Milakov, M., Park, J., Ionescu, R., Popescu, M., Grozea, C., Bergstra, J., Xie, J., Romaszko, L., Xu, B., Chuang, Z., & Bengio, Y.
- Affiliations
- Université de Montréal (CA)
- Google (United States) (US)
- Kaggle (US)
- University of Toronto (CA)
- University at Buffalo, State University of New York (US)
- Beijing University of Posts and Telecommunications (CN)
- University College London (GB)
- University of Bucharest (RO)
- University of Waterloo (CA)
- Corresponding author
- Goodfellow, Ian J.
- ORCID
- Venue
- Neural Networks
- Publisher
- Elsevier BV
- Published
- 2015-04
- Volume
- 64
- Pages
- 59-63
- ISSN
- 0893-6080 (ISSN-L)
- OpenAlex
- W2744909235
- Cited by
- 697
- References
- 20
- Citations by year
- 2026: 71 · 2025: 66 · 2024: 65 · 2023: 87 · 2022: 97 · 2021: 93 · 2020: 83 · 2019: 62 · 2018: 34 · 2017: 18 · 2016: 15 · 2015: 3 · 2014: 2
- FWCI
- 12.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neural Networks (publishedVersion)
- PubMed (publishedVersion)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- representation learning, machine learning competition, facial expression recognition, multimodal learning, benchmark dataset
- MeSH terms
- Algorithms, Artificial Intelligence, Humans, Biometric Identification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
