Don't fear the unlabelled: safe semi-supervised learning via simple debiasing
This work addresses the lack of theoretical guarantees in SSL for researchers and practitioners, offering an incremental improvement with practical applicability.
The paper tackled the problem of biased risk estimates in semi-supervised learning (SSL) that lack theoretical guarantees, proposing a simple debiasing method that provides generalization error bounds and competes with classic deep SSL techniques by delivering better calibrated models.
Semi-supervised learning (SSL) provides an effective means of leveraging unlabelled data to improve a model performance. Even though the domain has received a considerable amount of attention in the past years, most methods present the common drawback of lacking theoretical guarantees. Our starting point is to notice that the estimate of the risk that most discriminative SSL methods minimise is biased, even asymptotically. This bias impedes the use of standard statistical learning theory and can hurt empirical performance. We propose a simple way of removing the bias. Our debiasing approach is straightforward to implement and applicable to most deep SSL methods. We provide simple theoretical guarantees on the trustworthiness of these modified methods, without having to rely on the strong assumptions on the data distribution that SSL theory usually requires. In particular, we provide generalisation error bounds for the proposed methods. We evaluate debiased versions of different existing SSL methods, such as the Pseudo-label method and Fixmatch, and show that debiasing can compete with classic deep SSL techniques in various settings by providing better calibrated models. Additionally, we provide a theoretical explanation of the intuition of the popular SSL methods.