CRITICAL ANALYSIS OF SELF-SUPERVISION

Presented by

Maral Rasoolijaberi

Introduction

This paper evaluated the performance of state-of-the-art self-supervised (unsupervised) methods on learning weights of convolutional neural networks (CNNs) to figure out whether current self-supervision techniques can learn deep features from only one image. The main goal of self-supervised learning is to take advantage of vast amount of unlabeled data for training CNNs and finding a generalized image representation. In self-supervised learning, data generate ground truth labels per se by pretext tasks such as rotation estimation.