Want to improve accuracy and robustness of your model? Use unlabeled data! Our new work uses self-training on unlabeled data to achieve 87.4% top-1 on ImageNet, 1% better than SOTA. Huge gains are seen on harder benchmarks (ImageNet-A, C and P). Link: arxiv.org/abs/1911.04252 pic.twitter.com/0umSnX7wui

Example predictions on robustness benchmarks ImageNet-A, C and P. Black texts are correct predictions made by our model and red texts are incorrect predictions by our baseline model. pic.twitter.com/eem6tlfyPX

Full comparison against state-of-the-art on ImageNet. Noisy Student is our method. Noisy Student + EfficientNet is 11% better than your favorite ResNet-50 😉 pic.twitter.com/BhwgJvSOYK

Method is also super simple: 1) Train a classifier on ImageNet 2) Infer labels on a much larger unlabeled dataset 3) Train a larger classifier on the combined set 4) Iterate the process, adding noise