Anomaly Detection On One Class Cifar 10
评估指标
AUROC
评测结果
各个模型在此基准测试上的表现结果
比较表格
| 模型名称 | AUROC |
|---|---|
| gan-based-anomaly-detection-in-imbalance | 90.6 |
| explainable-deep-one-class-classification | 92 |
| unsupervised-anomaly-detection-and | 91.25 |
| generalad-anomaly-detection-across-domains-by | 99.3 |
| classification-based-anomaly-detection-for-1 | 88.2 |
| anomaly-detection-requires-better | 98.4 |
| using-self-supervised-learning-can-improve | 90.1 |
| csi-novelty-detection-via-contrastive | 94.3 |
| inverse-transform-autoencoder-for-anomaly | 86.6 |
| panda-adapting-pretrained-features-for | 96.2 |
| deep-nearest-neighbor-anomaly-detection | 92.5 |
| unsupervised-anomaly-detection-and | 74.33 |
| learning-and-evaluating-representations-for-1 | 92.5 |
| ssd-a-unified-framework-for-self-supervised-1 | 90.0 |
| panda-adapting-pretrained-features-for | 64.7 |
| oled-one-class-learned-encoder-decoder | 67.1 |
| transformaly-two-feature-spaces-are-better | 98.3 |
| mean-shifted-contrastive-loss-for-anomaly | 98.6 |
| unsupervised-two-stage-anomaly-detection | 88.4 |
| exposing-outlier-exposure-what-can-be-learned | 99.6 |
| ocgan-one-class-novelty-detection-using-gans | 66.83 |
| fake-it-till-you-make-it-near-distribution | 99.1 |
| deep-unsupervised-image-anomaly-detection-an | 92.6 |
| panda-adapting-pretrained-features-for | 64.8 |
| p-kdgan-progressive-knowledge-distillation | 73.05 |
| when-text-and-images-don-t-mix-bias | 99.1 |
| panda-adapting-pretrained-features-for | 98.9 |
| deep-anomaly-detection-using-geometric | 86 |
| fastflow-unsupervised-anomaly-detection-and | 66.7 |
| deep-one-class-classification | 65.7 |
| esad-end-to-end-deep-semi-supervised-anomaly | 83.3 |
| anomaly-detection-via-reverse-distillation | 86.5 |
| exposing-outlier-exposure-what-can-be-learned | 98.5 |
| unsupervised-anomaly-detection-and | 83.68 |
| simple-adaptive-projection-with-pretrained | 97.0 |
| dasvdd-deep-autoencoding-support-vector-data | 66.5 |