Semi supervised learning (SSL) provides an effective means of leveraging unlabelled data to improve a model's performance. Even though the domain has received a considerable amount of attention in the past years, most methods present the common drawback of being unsafe. By safeness we mean the quality of not degrading a fully supervised model when including unlabelled data. Our starting point is to notice that the estimate of the risk that most discriminative SSL methods minimise is biased, even asymptotically. This bias makes these techniques untrustable without a proper validation set, but 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 safeness of these modified methods, without having to rely on the strong assumptions on the data distribution that SSL theory usually requires. We evaluate debiased versions of different existing SSL methods and show that debiasing can compete with classic deep SSL techniques in various classic settings and even performs well when traditional SSL fails.

提出一种去偏移的方法，适用于多数深度半监督学习方法，并提供了简单的理论保证和推广误差界限，以在各种情境中提供更好的模型校准，同时说明了流行的SSL方法的直觉解释。

简单去偏置的安全半监督学习：不要害怕未标记的数据