In the last decade, deep supervised learning has had tremendous success. However, its
flaws, such as its dependency on manual and costly annotations on large datasets and …

Recently, Self-Supervised Representation Learning (SSRL) has attracted much attention in
the field of computer vision, speech, natural language processing (NLP), and recently, with …

Deep metric learning papers from the past four years have consistently claimed great
advances in accuracy, often more than doubling the performance of decade-old methods. In …

Existing metric learning losses can be categorized into two classes: pair-based and proxy-
based losses. The former class can leverage fine-grained semantic relations between data …

Optimising a ranking-based metric, such as Average Precision (AP), is notoriously
challenging due to the fact that it is non-differentiable, and hence cannot be optimised …

Abstract The Information Bottleneck (IB) provides an information theoretic principle for
representation learning, by retaining all information relevant for predicting label while …

We challenge a common assumption underlying most supervised deep learning: that a
model makes a prediction depending only on its parameters and the features of a single …

Abstract Deep Metric Learning (DML) is arguably one of the most influential lines of research
for learning visual similarities with many proposed approaches every year. Although the field …

Change Point Detection (CPD) methods identify the times associated with changes in the
trends and properties of time series data in order to describe the underlying behaviour of the …

Current contrastive learning frameworks focus on leveraging a single supervisory signal to
learn representations, which limits the efficacy on unseen data and downstream tasks. In this …