Hard Negative Mixing for Contrastive Learning

The uniformity experiment is based on Wang and Isola [53]. We follow the same definitions of the losses/metrics as presented in the paper. We set α = 2 and t = 2. All features were L2-normalized, as the metrics are defined on the hypersphere. B.1 Proxy task: Effect of MLP and Stronger Augmentation Following our discussion in Section 3, we wanted to verify that hardness of the proxy task for MoCo [19] is directly correlated to the difficulty of the transformations set, i.e. proxy task hardness can modulated via the positive pair.