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Recent methods for learning unsupervised visual representations, dubbedcontrastive learning, optimize the noise-contrastive estimation (NCE) bound onmutual information between two views of an image. NCE uses randomly samplednegative examples to normalize the objective. In this paper, we show thatchoosing difficult negatives, or those more similar to the current instance,can yield stronger representations. To do this, we introduce a family of mutualinformation estimators that sample negatives conditionally -- in a "ring"around each positive. We prove that these estimators lower-bound mutualinformation, with higher bias but lower variance than NCE. Experimentally, wefind our approach, applied on top of existing models (IR, CMC, and MoCo)improves accuracy by 2-5% points in each case, measured by linear evaluation onfour standard image datasets. Moreover, we find continued benefits whentransferring features to a variety of new image distributions from theMeta-Dataset collection and to a variety of downstream tasks such as objectdetection, instance segmentation, and keypoint detection.

Conditional Negative Sampling for Contrastive Learning of Visual Representations