Traditional supervised learning methods are hitting a bottleneck because of their
dependency on expensive manually labeled data and their weaknesses such as limited …

Recent progress in self-supervised learning has resulted in models that are capable of
extracting rich representations from image collections without requiring any explicit label …

Self-supervised learning (SSL) has delivered superior performance on a variety of
downstream vision tasks. Two main-stream SSL frameworks have been proposed, ie …

This paper enhances image-GPT (iGPT), one of the pioneering works that introduce
autoregressive pretraining to predict the next pixels for visual representation learning. Two …

A feature learning task involves training models that are capable of inferring good
representations (transformations of the original space) from input data alone. When working …

We focus on better understanding the critical factors of augmentation-invariant
representation learning. We revisit MoCo v2 and BYOL and try to prove the authenticity of …

Recently the state space models (SSMs) with efficient hardware-aware designs, ie, the
Mamba deep learning model, have shown great potential for long sequence modeling …

Self-supervised learning (especially contrastive learning) has attracted great interest due to
its huge potential in learning discriminative representations in an unsupervised manner …

Despite recent progress made by self-supervised methods in representation learning with
residual networks, they still underperform supervised learning on the ImageNet classification …

Collaborative learning enables distributed clients to learn a shared model for prediction
while keeping the training data local on each client. However, existing collaborative learning …