Mixture of Self-Supervised Learning

TL;DR

This paper introduces Gated Self-Supervised Learning, a method that combines multiple pretext tasks using a gating network to enhance image classification across various challenging scenarios.

Contribution

It proposes a novel Gated Self-Supervised Learning approach that integrates multiple pretext tasks with a Mixture of Experts architecture for improved image classification.

Findings

Enhanced performance on CIFAR imbalance datasets

Improved robustness against adversarial perturbations

Better class separation shown by Grad-CAM and T-SNE analyses

Abstract

Self-supervised learning is popular method because of its ability to learn features in images without using its labels and is able to overcome limited labeled datasets used in supervised learning. Self-supervised learning works by using a pretext task which will be trained on the model before being applied to a specific task. There are some examples of pretext tasks used in self-supervised learning in the field of image recognition, namely rotation prediction, solving jigsaw puzzles, and predicting relative positions on image. Previous studies have only used one type of transformation as a pretext task. This raises the question of how it affects if more than one pretext task is used and to use a gating network to combine all pretext tasks. Therefore, we propose the Gated Self-Supervised Learning method to improve image classification which use more than one transformation as pretext task and uses the Mixture of Expert architecture as a gating network in combining each pretext task so that the model automatically can study and focus more on the most useful augmentations for classification. We test performance of the proposed method in several scenarios, namely CIFAR imbalance dataset classification, adversarial perturbations, Tiny-Imagenet dataset classification, and semi-supervised learning. Moreover, there are Grad-CAM and T-SNE analysis that are used to see the proposed method for identifying important features that influence image classification and representing data for each class and separating different classes properly. Our code is in https://github.com/aristorenaldo/G-SSL