FROST: Faster and more Robust One-shot Semi-supervised Training

Abstract

Recent advances in one-shot semi-supervised learning have lowered the barrier for deep learning of new applications. However, the state-of-the-art for semi-supervised learning is slow to train and the performance is sensitive to the choices of the labeled data and hyper-parameter values. In this paper, we present a one-shot semi-supervised learning method that trains up to an order of magnitude faster and is more robust than state-of-the-art methods. Specifically, we show that by combining semi-supervised learning with a one-stage, single network version of self-training, our FROST methodology trains faster and is more robust to choices for the labeled samples and changes in hyper-parameters. Our experiments demonstrate FROST's capability to perform well when the composition of the unlabeled data is unknown; that is when the unlabeled data contain unequal numbers of each class and can contain out-of-distribution examples that don't belong to any of the training classes. High performance, speed of training, and insensitivity to hyper-parameters make FROST the most practical method for one-shot semi-supervised training. Our code is available at https://github.com/HelenaELiu/FROST.

arXiv commentWithdrawn because the results reported were due to an error in our code

References in corpus (18)

• A Simple Framework for Contrastive Learning of Visual Representations
• Bootstrap your own latent: A new approach to self-supervised Learning
• Improved Regularization of Convolutional Neural Networks with Cutout
• FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
• Improved Baselines with Momentum Contrastive Learning
• Wide Residual Networks
• Unsupervised Data Augmentation for Consistency Training
• Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
• Interpolation Consistency Training for Semi-Supervised Learning
• Contrastive Representation Learning: A Framework and Review
• Prototypical Contrastive Learning of Unsupervised Representations
• Big Self-Supervised Models are Strong Semi-Supervised Learners
• Billion-scale semi-supervised learning for image classification
• Shake-Shake regularization
• Self-training with Noisy Student improves ImageNet classification
• A survey on Semi-, Self- and Unsupervised Learning for Image Classification
• ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring
• Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks