python train.py --gpu 0,1,2,3 --save-path "./experiments/miniImageNet_MetaOptNet_SVM" --train-shot 15 \ --head SVM --network ResNet --dataset miniImageNet --eps 0.1 As shown in Figure 2, of our paper, we can meta-train the embedding once with a high shot for all meta-testi...
implicit differentiation of the optimality conditions of the convex problem and the dual formulation of the optimization problem. This allows us to use high-dimensional embeddings with improved generalization at a modest increase in computational overhead. Our approach, named MetaOptNet, achieves state-...