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Non-Contrastive Energy-Based, Self-Supervised Learning Methods
Non-contrastive energy-based, self-supervised learning methods look like? And why are they promising? So the idea basically is that you have this notion of a positive and a negative. In contrast to learning the quality of the negative sample really matters a lot. The other way which has become really popular is something called self distillation. It's all about similarity maximization between these two features.