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Anomaly Detection On Fashion Mnist

Metrics

ROC AUC

Results

Performance results of various models on this benchmark

Model Name
ROC AUC
Paper TitleRepository
Self-Supervised DeepSVDD84.8PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation-
P-KDGAN0.9293P-KDGAN: Progressive Knowledge Distillation with GANs for One-class Novelty Detection-
IGD (scratch)92.01Deep One-Class Classification via Interpolated Gaussian Descriptor-
Self-Supervised One-class SVM, RBF kernel92.8PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation-
GAN based Anomaly Detection in Imbalance Problems98.6GAN-based Anomaly Detection in Imbalance Problems-
Shell-based Anomaly (supervised)92.1Shell Theory: A Statistical Model of Reality
IGD (pre-trained ImageNet)93.57Deep One-Class Classification via Interpolated Gaussian Descriptor-
PANDA-OE91.8PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation-
Reverse Distillation95.0Anomaly Detection via Reverse Distillation from One-Class Embedding-
PANDA95.6PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation-
IGD (pre-trained SSL)94.41Deep One-Class Classification via Interpolated Gaussian Descriptor-
DASVDD92.6DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection-
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Anomaly Detection On Fashion Mnist | SOTA | HyperAI