Image Classification on Visual Decathlon VGG-Flowers held-out target
58.53AccuracyFeature-Critic
Evaluation results
| Method | Links | |
|---|---|---|
| Feature-CriticClassifier=SVM, Backbone=ResNet-182019.01 | 58.53 | |
| ReptileClassifier=SVM, Backbone=ResNet-182019.01 | 58.26 | |
| Data AggregationClassifier=SVM, Backbone=ResNet-182019.01 | 58.04 | |
| CrossGradClassifier=SVM, Backbone=ResNet-182019.01 | 57.84 | |
| MetaReg-FLClassifier=SVM, Backbone=ResNet-182019.01 | 53.04 | |
| ImageNet Pre-trainedClassifier=SVM, Backbone=ResNet-182019.01 | 51.57 | |
| CrossGradClassifier=KNN, Backbone=ResNet-182019.01 | 48 | |
| Feature-CriticClassifier=KNN, Backbone=ResNet-182019.01 | 47.04 | |
| Data AggregationClassifier=KNN, Backbone=ResNet-182019.01 | 45.98 | |
| ReptileClassifier=KNN, Backbone=ResNet-182019.01 | 45.8 | |
| ImageNet Pre-trainedClassifier=KNN, Backbone=ResNet-182019.01 | 41.08 | |
| MetaReg-FLClassifier=KNN, Backbone=ResNet-182019.01 | 39.51 | |
| MetaRegClassifier=SVM, Backbone=ResNet-182019.01 | 35.49 | |
| MetaRegClassifier=KNN, Backbone=ResNet-182019.01 | 23.63 |