Unsupervised representation learning approaches aim to learn discriminative feature
representations from unlabeled data, without the requirement of annotating every sample …

Despite the empirical successes of self-supervised learning (SSL) methods, it is unclear
what characteristics of their representations lead to high downstream accuracies. In this …

Negative sampling has swiftly risen to prominence as a focal point of research, with wide-
ranging applications spanning machine learning, computer vision, natural language …

The goal of contrasting learning is to learn a representation that preserves underlying
clusters by keeping samples with similar content, eg the “dogness” of a dog, close to each …

Contrastive representation learning encourages data representation to make semantically
similar pairs closer than randomly drawn negative samples, which has been successful in …

Multi-modal entity alignment aims to identify entities that refer to the same concept in the real
world across a plethora of multi-modal knowledge graphs (MMKGs). Most existing methods …

Deep learning (DL) applied to breast tissue segmentation in magnetic resonance imaging
(MRI) has received increased attention in the last decade, however, the domain shift which …

Cross-view consensus representation plays a critical role in hyperspectral images (HSIs)
clustering. Recent multi-view contrastive cluster methods utilize contrastive loss to extract …

Deep Neural Networks (DNNs) are susceptible to elaborately designed perturbations,
whether such perturbations are dependent or independent of images. The latter one, called …

Inference, especially those derived from inductive processes, is a crucial component in our
conversation to complement the information implicitly or explicitly conveyed by a speaker …