Can you link works in Computer Vision SSL which incorporate this principle “use model’s high confidence outputs on easy examples to train it on hard examples”? It is not obvious to me how this would work. For example, in contrastive learning the objective is to learn view invariant representations. Two views of an object, augmented differently, are pushed together in representation space by minimizing the distance between them as our loss function. Which one would constitute the easy/hard example here?