Contrastive learning has become a key component of self-supervised learning approaches
for computer vision. By learning to embed two augmented versions of the same image close …

Novelty detection, ie, identifying whether a given sample is drawn from outside the training
distribution, is essential for reliable machine learning. To this end, there have been many …

Transformers and masked language modeling are quickly being adopted and explored in
computer vision as vision transformers and masked image modeling (MIM). In this work, we …

Self-supervised learning has been shown to be very effective in learning useful
representations, and yet much of the success is achieved in data types such as images …

Deep learning has brought significant developments in image understanding tasks such as
object detection, image classification, and image segmentation. But the success of image …

We propose a framework for sequence-to-sequence contrastive learning (SeqCLR) of visual
representations, which we apply to text recognition. To account for the sequence-to …

Abstract Clinical 12-lead electrocardiography (ECG) is one of the most widely encountered
kinds of biosignals. Despite the increased availability of public ECG datasets, label scarcity …

One of the central elements of any causal inference is an object called structural causal
model (SCM), which represents a collection of mechanisms and exogenous sources of …

Contrastive learning relies on an assumption that positive pairs contain related views that
share certain underlying information about an instance, eg, patches of an image or co …

An ideal learned representation should display transferability and robustness. Supervised
contrastive learning (SupCon) is a promising method for training accurate models, but …