Exciting new paper out this week which combines mixup with unlabeled data and the results are really impressive!
In this work, we unify the current dominant approaches for semi-supervised learning to produce a new algorithm, MixMatch, that works by guessing low-entropy labels for data-augmented unlabeled examples and mixing labeled and unlabeled data using MixUp. We show that MixMatch obtains state-of-the-art results by a large margin across many datasets and labeled data amounts. For example, on CIFAR-10 with 250 labels, we reduce error rate by a factor of 4 (from 38% to 11%)
My fav section is in the ablation tests, where they compared MixMatch without MixUp 39.11 test error vs MixMatch with MixUp 11.80 test error (CIFAR10 250 labels, rest unlabeled)…this shows the power of MixUp especially when working with limited data!
MixMatch: A Holistic Approach to Semi-Supervised Learning