Minimal implementation of PAWS in TensorFlow
PAWS-TF
Implementation of Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples (PAWS) in TensorFlow (2.4.1).
PAWS introduces a simple way to combine a very small fraction of labeled data with a comparatively larger corpus of unlabeled data during pre-training. With its approach, it sets the state-of-the-art in semi-supervised learning (as of May 2021) beating methods like SimCLRV2, Meta Pseudo Labels that too with fewer parameters and a smaller pre-training schedule. For details, I recommend checking out the original paper as well as this blog post by the authors.
This repository implements and includes all the major bits proposed in PAWS in TensorFlow. The only major difference is that the pre-training and subsequent fine-tuning weren’t run for the original number of epochs (600 and 30