Challenges in Representation Learning: A report on three machine learning contests

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.

8 pages, 2 figures

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Cited by in corpus (20)

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• A Deep Learning Perspective on the Origin of Facial Expressions
• Real-time Convolutional Neural Networks for Emotion and Gender Classification
• HEU Emotion: A Large-scale Database for Multi-modal Emotion Recognition in the Wild
• Convolutional neural networks pretrained on large face recognition datasets for emotion classification from video
• Towards Understanding Sparse Filtering: A Theoretical Perspective
• FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition
• CAKE: Compact and Accurate K-dimensional representation of Emotion
• DAiSEE: Towards User Engagement Recognition in the Wild
• Feeding Hand-Crafted Features for Enhancing the Performance of Convolutional Neural Networks
• From Facial Expression Recognition to Interpersonal Relation Prediction
• Emotion Recognition in Speech using Cross-Modal Transfer in the Wild
• Visual Saliency Maps Can Apply to Facial Expression Recognition
• Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution
• WiFE: WiFi and Vision based Intelligent Facial-Gesture Emotion Recognition
• Feature-level and Model-level Audiovisual Fusion for Emotion Recognition in the Wild
• Disentanglement for Discriminative Visual Recognition
• Learning Non-Linear Feature Maps
• Deep Multi-Facial patches Aggregation Network for Expression Classification from Face Images
• Principled Non-Linear Feature Selection