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Pytorch Implementation for CVPR2018 Paper: Learning to Compare: Relation Network for Few-Shot Learning

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LearningToCompare

Pytorch Implementation for Paper: Learning to Compare: Relation Network for Few-Shot Learning

Howto

download mini-imagenet and make it looks like:

mini-imagenet/
├── images
	├── n0210891500001298.jpg  
	├── n0287152500001298.jpg 
	...
├── test.csv
├── val.csv
└── train.csv

LearningToCompare-Pytorch/
├── compare.py
├── MiniImagenet.py
├── Readme.md
├── repnet.py
├── train.py
└── utils.py
python train.py

NOTICE

current code support multi-gpus on single machine training, to disable it and train on single machine, just set device_ids=[0] and downsize batch size according to your gpu memory capacity. make sure ckpt directory exists, otherwise mkdir ckpt.

mini-Imagenet

Model Fine Tune 5-way Acc. 20-way Acc
1-shot 5-shot 1-shot 5-shot
Matching Nets N 43.56% 55.31% 17.31% 22.69%
Meta-LSTM 43.44% 60.60% 16.70% 26.06%
MAML Y 48.7% 63.11% 16.49% 19.29%
Meta-SGD 50.49% 64.03% 17.56% 28.92%
TCML 55.71% 68.88% - -
Learning to Compare N 57.02% 71.07% - -
Ours, similarity ensemble N 55.2% 68.8%
Ours, feature ensemble N 55.2% 70.1%

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Pytorch Implementation for CVPR2018 Paper: Learning to Compare: Relation Network for Few-Shot Learning

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