Meta-Learning with Differentiable Convex Optimization

This repository contains the code for the paper:
Meta-Learning with Differentiable Convex Optimization
Kwonjoon Lee, Subhransu Maji, Avinash Ravichandran, Stefano Soatto
CVPR 2019 (Oral)

Abstract

Many meta-learning approaches for few-shot learning rely on simple base learners such as nearest-neighbor classifiers. However, even in the few-shot regime, discriminatively trained linear predictors can offer better generalization. We propose to use these predictors as base learners to learn representations for few-shot learning and show they offer better tradeoffs between feature size and performance across a range of few-shot recognition benchmarks. Our objective is to learn feature embeddings that generalize well under a linear classification rule for novel categories. To efficiently solve the objective, we exploit two properties of linear classifiers: 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-of-the-art performance on miniImageNet, tieredImageNet, CIFAR-FS and FC100 few-shot learning benchmarks.

Citation

If you use this code for your research, please cite our paper:

@inproceedings{lee2019meta,
  title={Meta-Learning with Differentiable Convex Optimization},
  author={Kwonjoon Lee and Subhransu Maji and Avinash Ravichandran and Stefano Soatto},
  booktitle={CVPR},
  year={2019}
}

Dependencies

• Python 2.7+ (not tested on Python 3)
• PyTorch 0.4.0+
• qpth 0.0.11+
• tqdm

Usage

Installation

1. Clone this repository:

   git clone https://github.com/kjunelee/MetaOptNet.git
   cd MetaOptNet

2. Download and decompress dataset files: miniImageNet (courtesy of Spyros Gidaris), tieredImageNet, FC100, CIFAR-FS

3. For each dataset loader, specify the path to the directory. For example, in MetaOptNet/data/mini_imagenet.py line 30:

   _MINI_IMAGENET_DATASET_DIR = 'path/to/miniImageNet'

Meta-training

1. To train MetaOptNet-SVM on 5-way miniImageNet benchmark:

   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-testing shots. We don't need to meta-train with all possible meta-test shots unlike in Prototypical Networks.
2. You can experiment with varying base learners by changing '--head' argument to ProtoNet or Ridge. Also, you can change the backbone architecture to vanilla 4-layer conv net by setting '--network' argument to ProtoNet. For other arguments, please see MetaOptNet/train.py from lines 85 to 114.
3. To train MetaOptNet-SVM on 5-way tieredImageNet benchmark:

   python train.py --gpu 0,1,2,3 --save-path "./experiments/tieredImageNet_MetaOptNet_SVM" --train-shot 10 \
   --head SVM --network ResNet --dataset tieredImageNet

4. To train MetaOptNet-RR on 5-way CIFAR-FS benchmark:

   python train.py --gpu 0 --save-path "./experiments/CIFAR_FS_MetaOptNet_RR" --train-shot 5 \
   --head Ridge --network ResNet --dataset CIFAR_FS

5. To train MetaOptNet-RR on 5-way FC100 benchmark:

   python train.py --gpu 0 --save-path "./experiments/FC100_MetaOptNet_RR" --train-shot 15 \
   --head Ridge --network ResNet --dataset FC100

Meta-testing

1. To test MetaOptNet-SVM on 5-way miniImageNet 1-shot benchmark:

python test.py --gpu 0,1,2,3 --load ./experiments/miniImageNet_MetaOptNet_SVM/best_model.pth --episode 1000 \
--way 5 --shot 1 --query 15 --head SVM --network ResNet --dataset miniImageNet

2. Similarly, to test MetaOptNet-SVM on 5-way miniImageNet 5-shot benchmark:

python test.py --gpu 0,1,2,3 --load ./experiments/miniImageNet_MetaOptNet_SVM/best_model.pth --episode 1000 \
--way 5 --shot 5 --query 15 --head SVM --network ResNet --dataset miniImageNet

Acknowledgments

This code is based on the implementations of Prototypical Networks, Dynamic Few-Shot Visual Learning without Forgetting, and DropBlock.