Semi-supervised learning competition for course Deep Learning DS-GA 1008 (Spring 2019) at New York University. This course was taught by Prof. Yann LeCun. Below is a post by him about this competition. https://www.facebook.com/yann.lecun/posts/10155962251157143
Following dataset was given -
- unlabeled set: 512k images from 1000 classes from ImageNet 22k resized to 96x96 pixels.
- Labeled set 64k images from 1000 different classes from ImageNet 22k (64 images/category) resized to 96x96.
Task is to learn category detector from the given data (No pretrained models/external dataset is allowed)
We solved this problem by using Self-supervised learning, by predicting Image Rotations. Please see the following paper for more details -
https://arxiv.org/abs/1803.07728
https://github.com/gidariss/FeatureLearningRotNet
Libraries - Pytorch
Results
We achieved 61% Top-5 accuracy on validation set. The winning team had around 65% top accuracy. We were amongst top5 teams in the competition.