Challenges in Representation Learning: A report on three machine learning contests
Ian J. Goodfellow, Dumitru Erhan, Pierre Luc Carrier, Aaron Courville, Mehdi Mirza, Ben Hamner, Will Cukierski, Yichuan Tang, David Thaler, Dong-Hyun Lee, Yingbo Zhou, Chetan Ramaiah, Fangxiang Feng, Ruifan Li, Xiaojie Wang, Dimitris Athanasakis, John Shawe-Taylor, Maxim Milakov, John Park, Radu Ionescu, Marius Popescu, Cristian Grozea, James Bergstra, Jingjing Xie, Lukasz Romaszko, Bing Xu, Zhang Chuang, Yoshua Bengio
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/phamquiluan/ResidualMaskingNetworkpytorch★ 507
- github.com/elangarka/Regularized-Xception-FER-Extra-Training-Data-Step-Decay-Learning-Ratenone★ 3
- github.com/oellop/facial_expressiontf★ 1
- github.com/chinhau-lim/fer2013tf★ 0
- github.com/chinhau-lim/fer_2013tf★ 0
- github.com/hemanshanand/Facial-Expression-Recognitiontf★ 0
- github.com/jannine92/supervised_emotion_recognitiontf★ 0
- github.com/leorrose/Social-Network-Facial-Expression-Recognitiontf★ 0
- github.com/justinshenk/fertf★ 0
- github.com/NJNischal/Facial-Expression-Recognition-with-CNNstf★ 0
Abstract
The ICML 2013 Workshop on Challenges in Representation Learning 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.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| FER2013 | Local Learning BOW | Accuracy | 67.48 | — | Unverified |