Self-supervised learning algorithms based on instance discrimination train encoders to be
invariant to pre-defined transformations of the same instance. While most methods treat …

In self-supervised representation learning, a common idea behind most of the state-of-the-
art approaches is to enforce the robustness of the representations to predefined …

Recent studies on contrastive learning have achieved remarkable performance solely by
leveraging few labels in medical image segmentation. Existing methods mainly focus on …

Humans can easily perceive the direction of sound sources in a visual scene, termed sound
source localization. Recent studies on learning-based sound source localization have …

Contrastive self-supervised learning has largely narrowed the gap to supervised pre-training
on ImageNet. However, its success highly relies on the object-centric priors of ImageNet, ie …

Our ability to use deep learning approaches to decipher neural activity would likely benefit
from greater scale, in terms of both the model size and the datasets. However, the …

Vision-language models, such as contrastive language-image pre-training (CLIP), have
demonstrated impressive results in natural image domains. However, these models often …

Contrastive learning methods train visual encoders by comparing views from one instance to
others. Typically, the views created from one instance are set as positive, while views from …

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

Contrastive learning, which learns to contrast positive with negative pairs of samples, has
been popular for self-supervised visual representation learning. Although great effort has …