Self-supervised learning has emerged as a powerful technique for learning representations without the need for labeled data. SimCLR (Simple Framework for Contrastive Learning of Visual Representations, https://arxiv.org/abs/2002.05709) is one such approach that leverages contrastive learning to learn visual representations. This notebook outlines the implementation of SimCLR using the STL-10 dataset in PyTorch.
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Implementation of SimCLR for self-supervised learning of visual representations using the STL-10 dataset in PyTorch.
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