Self-supervised visual learning in the low-data regime: a comparative evaluation

TL;DR

This paper evaluates the effectiveness of self-supervised learning (SSL) in low-data scenarios for visual tasks, showing that domain-specific SSL pretraining can outperform large-scale general dataset pretraining.

Contribution

It provides a comparative analysis of modern SSL methods in low-data regimes, highlighting their performance and behavior in domain-specific applications.

Findings

In-domain low-data SSL pretraining often surpasses large-scale general dataset pretraining.

Different SSL categories exhibit varied behaviors in low-data settings.

SSL can effectively learn representations even with limited data in domain-specific tasks.

Abstract

Self-Supervised Learning (SSL) is a valuable and robust training methodology for contemporary Deep Neural Networks (DNNs), enabling unsupervised pretraining on a 'pretext task' that does not require ground-truth labels/annotation. This allows efficient representation learning from massive amounts of unlabeled training data, which in turn leads to increased accuracy in a 'downstream task' by exploiting supervised transfer learning. Despite the relatively straightforward conceptualization and applicability of SSL, it is not always feasible to collect and/or to utilize very large pretraining datasets, especially when it comes to real-world application settings. In particular, in cases of specialized and domain-specific application scenarios, it may not be achievable or practical to assemble a relevant image pretraining dataset in the order of millions of instances or it could be computationally infeasible to pretrain at this scale, e.g., due to unavailability of sufficient computational resources that SSL methods typically require to produce improved visual analysis results. This situation motivates an investigation on the effectiveness of common SSL pretext tasks, when the pretraining dataset is of relatively limited/constrained size. This work briefly introduces the main families of modern visual SSL methods and, subsequently, conducts a thorough comparative experimental evaluation in the low-data regime, targeting to identify: a) what is learnt via low-data SSL pretraining, and b) how do different SSL categories behave in such training scenarios. Interestingly, for domain-specific downstream tasks, in-domain low-data SSL pretraining outperforms the common approach of large-scale pretraining on general datasets.