TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Anomaly Detection	Anomaly Detection on Anomaly Detection on Unlabeled ImageNet-30 vs Flowers-102	GOAD	ROC-AUC	92.8	# 4
Anomaly Detection	Anomaly Detection on Anomaly Detection on Unlabeled ImageNet-30 vs Flowers-102	GOAD	Network	ResNet-18	# 1
Anomaly Detection	Anomaly Detection on Unlabeled CIFAR-10 vs LSUN (Fix)	GOAD	ROC-AUC	78.8	# 8
Anomaly Detection	Anomaly Detection on Unlabeled CIFAR-10 vs LSUN (Fix)	GOAD	Network	ResNet-18	# 1
Anomaly Detection	Anomaly Detection on Unlabeled ImageNet-30 vs CUB-200	GOAD	ROC-AUC	90.5	# 3
Anomaly Detection	Anomaly Detection on Unlabeled ImageNet-30 vs CUB-200	GOAD	Network	ResNet-18	# 1
Anomaly Detection	One-class CIFAR-10	GOAD	AUROC	88.2	# 20
Anomaly Detection	UEA time-series datasets	GOAD	Avg. ROC-AUC	87.2	# 2
Anomaly Detection	Unlabeled CIFAR-10 vs CIFAR-100	GOAD	AUROC	89.2	# 8
Anomaly Detection	Unlabeled CIFAR-10 vs CIFAR-100	GOAD	Network	ResNet-18	# 1

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Classification-Based Anomaly Detection for General Data

ICLR 2020 · Liron Bergman, Yedid Hoshen ·

Anomaly detection, finding patterns that substantially deviate from those seen previously, is one of the fundamental problems of artificial intelligence. Recently, classification-based methods were shown to achieve superior results on this task. In this work, we present a unifying view and propose an open-set method, GOAD, to relax current generalization assumptions. Furthermore, we extend the applicability of transformation-based methods to non-image data using random affine transformations. Our method is shown to obtain state-of-the-art accuracy and is applicable to broad data types. The strong performance of our method is extensively validated on multiple datasets from different domains.

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xuhongzuo/DeepOD

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lironber/GOAD

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Anomaly Detection

Classification

General Classification

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CIFAR-10

CIFAR-100 UEA time-series datasets

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Ranked #2 on Anomaly Detection on UEA time-series datasets

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Task	Dataset	Model	Metric Name	Metric Value	Global Rank	Benchmark
Anomaly Detection	Anomaly Detection on Anomaly Detection on Unlabeled ImageNet-30 vs Flowers-102	GOAD	ROC-AUC	92.8	# 4	Compare
Anomaly Detection		GOAD	Network	ResNet-18	# 1	Compare
Anomaly Detection	Anomaly Detection on Unlabeled CIFAR-10 vs LSUN (Fix)	GOAD	ROC-AUC	78.8	# 8	Compare
Anomaly Detection	Anomaly Detection on Unlabeled CIFAR-10 vs LSUN (Fix)	GOAD	Network	ResNet-18	# 1	Compare
Anomaly Detection	Anomaly Detection on Unlabeled ImageNet-30 vs CUB-200	GOAD	ROC-AUC	90.5	# 3	Compare
Anomaly Detection	Anomaly Detection on Unlabeled ImageNet-30 vs CUB-200	GOAD	Network	ResNet-18	# 1	Compare
Anomaly Detection	One-class CIFAR-10	GOAD	AUROC	88.2	# 20	Compare
Anomaly Detection	UEA time-series datasets	GOAD	Avg. ROC-AUC	87.2	# 2	Compare
Anomaly Detection	Unlabeled CIFAR-10 vs CIFAR-100	GOAD	AUROC	89.2	# 8	Compare
Anomaly Detection	Unlabeled CIFAR-10 vs CIFAR-100	GOAD	Network	ResNet-18	# 1	Compare

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