SSL vs Supervised Character Classification

This project explores the effectiveness of self-supervised learning (SSL) methods, specifically SimCLR, in improving image classification performance compared to traditional supervised learning (SL) approaches on a restricted labeled dataset of 9600 Genshin Impact character images. It heavily relies on the ResNet-18 architecture as its backbone model.