Out-of-Distribution Detection Without Class Labels

14 Dec 2021  ·  Niv Cohen, Ron Abutbul, Yedid Hoshen ·

Out-of-distribution detection seeks to identify novelties, samples that deviate from the norm. The task has been found to be quite challenging, particularly in the case where the normal data distribution consists of multiple semantic classes (e.g., multiple object categories). To overcome this challenge, current approaches require manual labeling of the normal images provided during training. In this work, we tackle multi-class novelty detection without class labels. Our simple but effective solution consists of two stages: we first discover "pseudo-class" labels using unsupervised clustering. Then using these pseudo-class labels, we are able to use standard supervised out-of-distribution detection methods. We verify the performance of our method by a favorable comparison to the state-of-the-art, and provide extensive analysis and ablations.

PDF Abstract
Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Anomaly Detection Anomaly Detection on Anomaly Detection on Unlabeled ImageNet-30 vs Flowers-102 PsudoLabels CLIP ViT ROC-AUC 98.3 # 1
Network ViT # 1
Anomaly Detection Anomaly Detection on Unlabeled CIFAR-10 vs LSUN (Fix) SCAN Features ROC-AUC 90.2 # 7
Network ResNet-18 # 1
Anomaly Detection Anomaly Detection on Unlabeled CIFAR-10 vs LSUN (Fix) PsudoLabels ResNet-18 ROC-AUC 94.3 # 4
Network ResNet-18 # 1
Anomaly Detection Anomaly Detection on Unlabeled CIFAR-10 vs LSUN (Fix) PsudoLabels ResNet-152 ROC-AUC 95.7 # 3
Network ResNet-152 # 1
Anomaly Detection Anomaly Detection on Unlabeled CIFAR-10 vs LSUN (Fix) PsudoLabels ViT ROC-AUC 99.1 # 1
Network ViT # 1
Anomaly Detection Anomaly Detection on Unlabeled ImageNet-30 vs CUB-200 PsudoLabels CLIP ViT ROC-AUC 99.4 # 1
Network ViT # 1
Anomaly Detection Unlabeled CIFAR-10 vs CIFAR-100 PsudoLabels ResNet-152 AUROC 93.3 # 2
Network ResNet-152 # 1
Anomaly Detection Unlabeled CIFAR-10 vs CIFAR-100 PsudoLabels ResNet-18 AUROC 90.8 # 3
Network ResNet-18 # 1
Anomaly Detection Unlabeled CIFAR-10 vs CIFAR-100 PsudoLabels ViT AUROC 96.7 # 1
Network ViT # 1
Anomaly Detection Unlabeled CIFAR-10 vs CIFAR-100 SCAN Features AUROC 90.2 # 4
Network ResNet-18 # 1

Methods


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