SimCLR: A Simple Framework for Contrastive Learning of Visual Representations
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Project Overview ‹ SeqCLR: Self-supervised learning of features for time-series data – MIT Media Lab
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AI • Machine Learning • Tech on Instagram: "SimCLR (Simple Contrastive Learning of Representations) is a technique for self-supervised learning, primarily designed to learn representations from unlabeled data. The main idea behind SimCLR is to use contrastive learning to map similar data points closer together in the representation space while pushing dissimilar ones apart. This is achieved by creating augmented views of the same image and training a neural network to recognize that these augmen
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