Image Classification on Visual Decathlon D. Textures held-out target
41.7AccuracyImageNet Pre-trained
Evaluation Results
| Method | Links | |
|---|---|---|
| ImageNet Pre-trainedClassifier=SVM, Backbone=ResNet-182019.01 | 41.7 | |
| MetaRegClassifier=KNN, Backbone=ResNet-182019.01 | 39.41 | |
| Feature-CriticClassifier=SVM, Backbone=ResNet-182019.01 | 38.88 | |
| ReptileClassifier=SVM, Backbone=ResNet-182019.01 | 37.39 | |
| Feature-CriticClassifier=KNN, Backbone=ResNet-182019.01 | 37.01 | |
| CrossGradClassifier=SVM, Backbone=ResNet-182019.01 | 36.54 | |
| Data AggregationClassifier=SVM, Backbone=ResNet-182019.01 | 36.49 | |
| MetaReg-FLClassifier=SVM, Backbone=ResNet-182019.01 | 35.69 | |
| Data AggregationClassifier=KNN, Backbone=ResNet-182019.01 | 34.92 | |
| MetaReg-FLClassifier=KNN, Backbone=ResNet-182019.01 | 32.8 | |
| MetaRegClassifier=SVM, Backbone=ResNet-182019.01 | 32.34 | |
| ReptileClassifier=KNN, Backbone=ResNet-182019.01 | 32.02 | |
| ImageNet Pre-trainedClassifier=KNN, Backbone=ResNet-182019.01 | 31.98 | |
| CrossGradClassifier=KNN, Backbone=ResNet-182019.01 | 27.93 |