Survey of self-supervised learning using images for generative and discriminative training
Survey of self-supervised learning using images for generative and discriminative training
Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training
arXiv paper abstract https://arxiv.org/abs/2305.13689
arXiv PDF paper https://arxiv.org/pdf/2305.13689.pdf
Although supervised learning has been ... successful in ... computer vision in the past, the ... improvement has diminished ... in recent years
... Inspired by ... NLP, self-supervised methods that rely on clustering, contrastive learning, distillation, and information-maximization, which all fall under ... discriminative SSL, have experienced a swift uptake in the area of computer vision.
Shortly afterwards, generative SSL frameworks that are mostly based on masked image modeling, complemented and surpassed the results obtained with discriminative SSL.
Consequently, within a span of three years, over 100 unique general-purpose frameworks for generative and discriminative SSL, with a focus on imaging, were proposed.
In this survey ... review ... research efforts conducted on image-oriented SSL, providing a historic view and paying attention to best practices as well as useful software packages.
... discuss pretext tasks for image-based SSL, as well as techniques that are commonly used in image-based SSL ...
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