“Billion-Scale Semi-Supervised Learning for Image Classification”, 2019-05-02 (; similar):
This paper presents a study of semi-supervised learning with large convolutional networks. We propose a pipeline, based on a teacher/student paradigm, that leverages a large collection of unlabeled images (up to 1 billion). Our main goal is to improve the performance for a given target architecture, like ResNet-50 or ResNext.
We provide an extensive analysis of the success factors of our approach, which leads us to formulate some recommendations to produce high-accuracy models for image classification with semi-supervised learning.
As a result, our approach brings important gains to standard architectures for image, video and fine-grained classification. For instance, by leveraging one billion unlabeled images, our learned vanilla ResNet-50 achieves 81.2% top-1 accuracy on the ImageNet benchmark.