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Semi-Supervising Learning, Transfer Learning, and Knowledge Distillation with SimCLR
Most successful semi-supervised learning approaches in computer vision focus on leveraging huge amount of unlabeled data, learning the general representation via data augmentation and transformation, creating pseudo labels, implementing different loss functions, and eventually transferring this knowledge to more task-specific smaller models.
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