Anomaly Detection On One Class Cifar 10

Metrics

AUROC

Results

Performance results of various models on this benchmark

Model Name	AUROC	Paper Title	Repository
GAN based Anomaly Detection in Imbalance Problems	90.6	GAN-based Anomaly Detection in Imbalance Problems	-
FCDD	92	Explainable Deep One-Class Classification
IGD (pre-trained SSL)	91.25	Deep One-Class Classification via Interpolated Gaussian Descriptor
GeneralAD	99.3	GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features
GOAD	88.2	Classification-Based Anomaly Detection for General Data
DINO-FT	98.4	Anomaly Detection Requires Better Representations
SSOOD	90.1	Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
CSI	94.3	CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
ARNET	86.6	Attribute Restoration Framework for Anomaly Detection
PANDA	96.2	PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
DN2	92.5	Deep Nearest Neighbor Anomaly Detection	-
IGD (scratch)	74.33	Deep One-Class Classification via Interpolated Gaussian Descriptor
DisAug CLR	92.5	Learning and Evaluating Representations for Deep One-class Classification
SSD	90.0	SSD: A Unified Framework for Self-Supervised Outlier Detection
Self-Supervised One-class SVM, RBF kernel	64.7	PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
OLED	67.1	OLED: One-Class Learned Encoder-Decoder Network with Adversarial Context Masking for Novelty Detection
Transformaly	98.3	Transformaly -- Two (Feature Spaces) Are Better Than One
Mean-Shifted Contrastive Loss	98.6	Mean-Shifted Contrastive Loss for Anomaly Detection
UTAD	88.4	Unsupervised Two-Stage Anomaly Detection	-
CLIP (OE)	99.6	Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images

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