Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …

This paper demonstrates an approach for learning highly semantic image representations
without relying on hand-crafted data-augmentations. We introduce the Image-based Joint …

Semi-supervised learning lately has shown much promise in improving deep learning
models when labeled data is scarce. Common among recent approaches is the use of …

In this work, we perform unsupervised learning of representations by maximizing mutual
information between an input and the output of a deep neural network encoder. Importantly …

Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from a
labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA …

In this paper, we propose an online clustering method called Contrastive Clustering (CC)
which explicitly performs the instance-and cluster-level contrastive learning. To be specific …

We present a novel clustering objective that learns a neural network classifier from scratch,
given only unlabelled data samples. The model discovers clusters that accurately match …

Can we automatically group images into semantically meaningful clusters when ground-
truth annotations are absent? The task of unsupervised image classification remains an …

Many recent methods for unsupervised or self-supervised representation learning train
feature extractors by maximizing an estimate of the mutual information (MI) between different …