HyperAIHyperAI

Command Palette

Search for a command to run...

Console

Anomaly Detection On One Class Cifar 10

Metrics

AUROC

Results

Performance results of various models on this benchmark

Paper Title
CLIP (OE)99.6Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
GeneralAD99.3GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features
Fake It Till You Make It99.1Fake It Till You Make It: Towards Accurate Near-Distribution Novelty Detection
BLISS99.1When Text and Images Don't Mix: Bias-Correcting Language-Image Similarity Scores for Anomaly Detection
PANDA-OE98.9PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
Mean-Shifted Contrastive Loss98.6Mean-Shifted Contrastive Loss for Anomaly Detection
CLIP (zero shot)98.5Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
DINO-FT98.4Anomaly Detection Requires Better Representations
Transformaly98.3Transformaly -- Two (Feature Spaces) Are Better Than One
CAP97.0Constrained Adaptive Projection with Pretrained Features for Anomaly Detection
PANDA96.2PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
CSI94.3CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
DUIAD92.6Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework
DN292.5Deep Nearest Neighbor Anomaly Detection
DisAug CLR92.5Learning and Evaluating Representations for Deep One-class Classification
FCDD92Explainable Deep One-Class Classification
IGD (pre-trained SSL)91.25Deep One-Class Classification via Interpolated Gaussian Descriptor
GAN based Anomaly Detection in Imbalance Problems90.6GAN-based Anomaly Detection in Imbalance Problems
SSOOD90.1Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
SSD90.0SSD: A Unified Framework for Self-Supervised Outlier Detection
0 of 36 row(s) selected.