Self-Supervised Learning for Large-Scale Unsupervised Image Clustering
Self-Supervised Learning for Large-Scale Unsupervised Image Clustering is a scholarly work, published in 2020. The main subjects of the publication include computer science, artificial intelligence, self-supervised learning, machine learning, transfer learning, unsupervised learning, set, semi-supervised learning, code, question answering, feature learning, feature engineering, process, artificial neural network, political representation, Competitive learning, pattern recognition, cluster analysis, and supervised learning. The authors propose a simple scheme for unsupervised classification based on self-supervised representations.