Comparing self-supervised methods · MAAI7602 Applied Computer Vision

Self-supervised methods differ in what they predict, where gradients flow, whether they need negatives, and how they avoid collapse. The right choice depends on the data, the task and the compute available. How do self-supervised methods differ, and how should one be chosen for a new domain?

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