There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average
Presently the most successful approaches to semi-supervised learning are based on consistency regularization, whereby a model is trained to be robust to small perturbations of its inputs and parameters.To understand consistency regularization, we conceptually explore how loss geometry interacts with training procedures.The consistency loss dramatically improves generalization performance over supervised-only training; however, we show that SGD struggles to converge on the consistency loss and continues to make large steps that lead to changes in predictions on the test data.Motivated by these observations, we propose to train consistency-based methods with Stochastic Weight Averaging, a recent approach which averages weights along the trajectory of SGD with a modified learning rate schedule.We also propose fast-SWA, which further accelerates convergence by averaging multiple points within each cycle of a cyclical learning rate schedule.With weight averaging, we achieve the best known semi-supervised results on CIFAR-10 and CIFAR-100, over many different quantities of labeled training data.For example, we achieve 5.0% error on CIFAR-10 with only 4000 labels, compared to the previous best result in the literature of 6.3%.
Consistency-based models for semi-supervised learning do not converge to a single point but continue to explore a diverse set of plausible solutions on the perimeter of a flat region. Weight averaging helps improve generalization performance.The paper proposes to apply Stochastic Weight Averaging to the semi-supervised learning context, arguing that the semi-supervised MT/Pi models are especially amenable to SWA and propose fast SWA to speed up training.