Hard Negative Mixing for Contrastive Learning

ImageNet-100 labels, to define the positive samples. In Figure 1, we track the proxy task performance when progressively moving from MoCo to MoCo-v2, i.e . Figure 1 are for the same τ = 0 .2 . B.2 Hard negative mixing variants not discussed in the main text While developing MoCHi, we considered a number of different mixing strategies in feature space. We found the two strategies presented in Sections 4.1 and 4.2 of the main paper to For MoCHi, the "top" negatives are defined via the negative For MoCHi, in Section 4.2 we propose to synthesize MoCHi samples according to the percentage of the query they have.