Self-supervised learning (SSL) as an effective paradigm of representation learning has
achieved tremendous success on various curated datasets in diverse scenarios …

Existing out-of-distribution (OOD) methods have shown great success on balanced datasets
but become ineffective in long-tailed recognition (LTR) scenarios where 1) OOD samples …

Machine learning algorithms learned from data with skewed distributions usually suffer from
poor generalization, especially when minority classes matter as much as, or even more than …

In federated learning (FL), the multi-step update and data heterogeneity among clients often
lead to a loss landscape with sharper minima, degenerating the performance of the resulted …

In scenarios with long-tailed distributions, the model's ability to identify tail classes is limited
due to the under-representation of tail samples. Class rebalancing, information …

Data mixing methods play a crucial role in semi-supervised learning (SSL) but their
application is unexplored in long-tailed semi-supervised learning (LTSSL). The primary …

Differing from traditional semi-supervised learning, class-imbalanced semi-supervised
learning presents two distinct challenges:(1) The imbalanced distribution of training samples …

Partial Label Learning (PLL) is a type of weakly supervised learning where each training
instance is assigned a set of candidate labels, but only one label is the ground-truth …

Addressing imbalanced or long-tailed data is a major challenge in visual recognition tasks
due to disparities between training and testing distributions and issues with data noise. We …

In federated learning (FL), data heterogeneity is one key bottleneck that causes model
divergence and limits performance. Addressing this, existing methods often regard data …