Self Supervised Learning

Representation Learning | Contrastive Learning | Pretext | Downstream | SimCLR | Machine Learning | Deep Learning

Representation Learning | Contrastive Learning | Pretext | Downstream | SimCLR | Machine Learning | Deep Learning

Master the Power of Unlabelled Data: Self-Supervised Machine Learning Techniques in Python for Artificial Intelligence

Self-Supervised Learning Including Supervised & Unsupervised (SimCLR) Contrastive Learning

Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled.

The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.*

Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks.