Direct coloring regularizes self-supervised learning by matching an intermediate representation's cross-correlation to a VAE-derived target, improving ImageNet linear accuracy while helping avoid collapse.
A simple framework for contrastive learning of visual representations
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Direct Coloring for Self-Supervised Enhanced Feature Decoupling
Direct coloring regularizes self-supervised learning by matching an intermediate representation's cross-correlation to a VAE-derived target, improving ImageNet linear accuracy while helping avoid collapse.