Deep neural networks excel in supervised learning tasks but are constrained by the need for
extensive labeled data. Self-supervised learning emerges as a promising alternative …

In a wide range of multimodal tasks, contrastive learning has become a particularly
appealing approach since it can successfully learn representations from abundant …

Anomaly detection seeks to identify unusual phenomena, a central task in science and
industry. The task is inherently unsupervised as anomalies are unexpected and unknown …

In this paper, we provide an information-theoretic perspective on Variance-Invariance-
Covariance Regularization (VICReg) for self-supervised learning. To do so, we first …

Diffusion models have revolutionized image editing but often generate images that violate
physical laws, particularly the effects of objects on the scene, eg, occlusions, shadows, and …

Anomaly detection (AD) is the machine learning task of identifying highly discrepant
abnormal samples by solely relying on the consistency of the normal training samples …

Anomaly detection methods strive to discover patterns that differ from the norm in a
meaningful way. This goal is ambiguous as different human operators may find different …

Anomaly detection methods strive to discover patterns that differ from the norm in a semantic
way. This goal is ambiguous as a data point differing from the norm by an attribute eg, age …

Building multisensory AI systems that learn from multiple sensory inputs such as text,
speech, video, real-world sensors, wearable devices, and medical data holds great promise …

Developing meaningful and efficient representations that separate the fundamental structure
of the data generation mechanism is crucial in representation learning. However …