Robust Semi-Supervised Learning with Out of Distribution Data

Semi-supervised learning (SSL) based on deep neural networks (DNNs) has recently been proven effective. However, recent work [Oliver et al., 2018] shows that the performance of SSL could degrade substantially when the unlabeled set has out-of-distribution examples (OODs). In this work, we first study the key causes about the negative impact of OOD on SSL. We found that (1) OODs close to the decision boundary have a larger effect on the performance of existing SSL algorithms than the OODs far away from the decision boundary and (2) Batch Normalization (BN), a popular module in deep networks, could degrade the performance of a DNN for SSL substantially when the unlabeled set contains OODs. To address these causes, we proposed a novel unified robust SSL approach for many existing SSL algorithms in order to improve their robustness against OODs. In particular, we proposed a simple modification to batch normalization, called weighted batch normalization, capable of improving the robustness of BN against OODs. We developed two efficient hyperparameter optimization algorithms that have different tradeoffs in computational efficiency and accuracy. The first is meta-approximation and the second is implicit-differentiation based approximation. Both algorithms learn to reweight the unlabeled samples in order to improve the robustness of SSL against OODs. Extensive experiments on both synthetic and real-world datasets demonstrate that our proposed approach significantly improves the robustness of four representative SSL algorithms against OODs, in comparison with four state-of-the-art robust SSL approaches. We performed an ablation study to demonstrate which components of our approach are most important for its success.