Abstract Semi-Supervised Learning (SSL) has been an effective way to leverage abundant
unlabeled data with extremely scarce labeled data. However, most SSL methods are …

Open-set Semi-supervised Learning (OSSL) holds a realistic setting that unlabeled data
may come from classes unseen in the labeled set, ie, out-of-distribution (OOD) data, which …

Semi-supervised learning (SSL) has been a fundamental challenge in machine learning for
decades. The primary family of SSL algorithms, known as pseudo-labeling, involves …

Robust generalization aims to tackle the most challenging data distributions which are rare
in the training set and contain severe noises, ie, photon-limited corruptions. Common …

Machine learning models are vulnerable to Out-Of-Distribution (OOD) examples, and such a
problem has drawn much attention. However, current methods lack a full understanding of …

Semi-supervised learning (SSL) methods effectively leverage unlabeled data to improve
model generalization. However, SSL models often underperform in open-set scenarios …

Abstract Out-of-Distribution (OOD) Generalization aims to learn robust models that
generalize well to various environments without fitting to distribution-specific features …

Data-free knowledge distillation aims to learn a small student network from a large pre-
trained teacher network without the aid of original training data. Recent works propose to …

Consistency regularization has achieved great successes in Semi-Supervised Single-Label
Image Classification (SS-SLC) with deep learning models, while few effort has been devoted …

Open-set learning deals with the testing distribution where there exist samples from the
classes that are unseen during training. They aim to classify the seen classes and recognize …