AutoNovel: Automatically Discovering and Learning Novel Visual Categories

TL;DR

AutoNovel introduces a self-supervised, joint learning approach for discovering and learning new visual categories in image collections, outperforming existing methods and capable of unsupervised clustering without prior class number knowledge.

Contribution

It proposes a novel method combining self-supervised learning, ranking-based knowledge transfer, and joint optimization for improved novel category discovery.

Findings

Outperforms current methods on standard benchmarks

Effective in fully unsupervised image clustering

Estimates the number of new categories without prior knowledge

Abstract

We tackle the problem of discovering novel classes in an image collection given labelled examples of other classes. We present a new approach called AutoNovel to address this problem by combining three ideas: (1) we suggest that the common approach of bootstrapping an image representation using the labelled data only introduces an unwanted bias, and that this can be avoided by using self-supervised learning to train the representation from scratch on the union of labelled and unlabelled data; (2) we use ranking statistics to transfer the model's knowledge of the labelled classes to the problem of clustering the unlabelled images; and, (3) we train the data representation by optimizing a joint objective function on the labelled and unlabelled subsets of the data, improving both the supervised classification of the labelled data, and the clustering of the unlabelled data. Moreover, we propose a method to estimate the number of classes for the case where the number of new categories is not known a priori. We evaluate AutoNovel on standard classification benchmarks and substantially outperform current methods for novel category discovery. In addition, we also show that AutoNovel can be used for fully unsupervised image clustering, achieving promising results.