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The goal of contrastive representation learning is to learn such an embedding space in which similar sample pairs stay close to each other while dissimilar ones are far apart. Contrastive learning can be applied to both supervised and unsupervised settings. When working with unsupervised data, contrastive learning is one of the most powerful approaches in self-supervised learning. Contrastive Training Objectives In early versions of loss functions for contrastive learning, only one positive and one negative sample are involved.

SIIA Announces Education Technology Finalists for 2021 CODiE Awards 152 education technology and service products recognized in peer-reviewed awards program News provided by Share this article WASHINGTON, May 5, 2021 /PRNewswire/ -- In celebration of the 36th annual SIIA CODiE Awards, the Software & Information Industry Association (SIIA) announces the 152 education technology finalists. Finalists, selected by expert reviewers, represent the most innovative and impactful products from technology developers, online learning services and related technologies. "The CODiE Awards recognize the mo...