Anomaly Detection On Mnist
Metrics
ROC AUC
Results
Performance results of various models on this benchmark
Model Name | ROC AUC | Paper Title | Repository |
|---|---|---|---|
| IGD (pre-trained ImageNet) | 99.27 | Deep One-Class Classification via Interpolated Gaussian Descriptor | |
| IGD (scratch) | 98.69 | Deep One-Class Classification via Interpolated Gaussian Descriptor | |
| LIS-AE | 97.68 | Latent-Insensitive autoencoders for Anomaly Detection | - |
| DASVDD | 97.7 | DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection | |
| GAN-based Anomaly Detection in Imbalance Problems | 99.7 | GAN-based Anomaly Detection in Imbalance Problems | - |
| P-KDGAN | 97.25 | P-KDGAN: Progressive Knowledge Distillation with GANs for One-class Novelty Detection | - |
0 of 6 row(s) selected.