With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations

Self-supervised learning algorithms based on instance discrimination train encoders to be invariant to pre-defined transformations of the same instance. While most methods treat different views of the same image as positives for a contrastive loss, we are interested in using positives from other instances in the dataset. Our method, Nearest-Neighbor Contrastive Learning of visual Representations (NNCLR), samples the nearest neighbors from the dataset in the latent space, and treats them as positives. This provides more semantic variations than pre-defined transformations. We find that using the nearest-neighbor as positive in contrastive losses improves performance significantly on ImageNet classification, from 71.7% to 75.6%, outperforming previous state-of-the-art methods. On semi-supervised learning benchmarks we improve performance significantly when only 1% ImageNet labels are available, from 53.8% to 56.5%. On transfer learning benchmarks our method outperforms state-of-the-art methods (including supervised learning with ImageNet) on 8 out of 12 downstream datasets. Furthermore, we demonstrate empirically that our method is less reliant on complex data augmentations. We see a relative reduction of only 2.1% ImageNet Top-1 accuracy when we train using only random crops.

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Fine-Grained Image Classification Birdsnap NNCLR Accuracy 61.4% # 5
Fine-Grained Image Classification Caltech-101 NNCLR Top-1 Error Rate 8.7% # 6
Image Classification CIFAR-10 NNCLR Percentage correct 93.7 # 138
Image Classification CIFAR-100 NNCLR Percentage correct 79 # 106
Image Classification DTD NNCLR Accuracy 75.5 # 5
Fine-Grained Image Classification FGVC Aircraft NNCLR Accuracy 64.1% # 38
Image Classification Flowers-102 NNCLR Accuracy 95.1 # 40
Image Classification Food-101 NNCLR Accuracy (%) 76.7 # 4
Self-Supervised Image Classification ImageNet NNCLR (ResNet-50, multi-crop) Top 1 Accuracy 75.6% # 47
Top 5 Accuracy 92.4 # 13
Semi-Supervised Image Classification ImageNet - 10% labeled data NNCLR (ResNet-50) Top 5 Accuracy 89.3% # 20
Top 1 Accuracy 69.8% # 22
Semi-Supervised Image Classification ImageNet - 1% labeled data NNCLR (ResNet-50) Top 5 Accuracy 80.7% # 18
Top 1 Accuracy 56.4% # 27
Image Classification Oxford-IIIT Pets NNCLR Accuracy 91.8 # 8
Image Classification PASCAL VOC 2007 NNCLR Accuracy 83 # 1
Image Classification Stanford Cars NNCLR Accuracy 67.1 # 19
Fine-Grained Image Classification SUN397 NNCLR Accuracy 62.5 # 4

Methods