l2t-ww's Introduction

Learning What and Where to Transfer (ICML 2019)

Learning What and Where to Transfer (ICML 2019) https://arxiv.org/abs/1905.05901

Requirements

python>=3.6
pytorch>=1.0
torchvision
cuda>=9.0

Note. The reported results in our paper were obtained in the old-version pytorch (pytorch=1.0, cuda=9.0). We recently executed again the experiment commands as described below using the recent version (pytorch=1.6.0, torchvision=0.7.0, cuda=10.1), and obtained similar results as reported in the paper.

Prepare Datasets

You can download CUB-200 and Stanford Dogs datasets

CUB-200: from http://www.vision.caltech.edu/visipedia/CUB-200-2011.html
Stanford Dogs: http://vision.stanford.edu/aditya86/ImageNetDogs/

You need to run the below pre-processing script for DataLoader.

python cub200.py /data/CUB_200_2011
python dog.py /data/dog

Train L2T-ww

You can train L2T-ww models with the same settings in our paper.

python train_l2t_ww.py --dataset cub200 --datasplit cub200 --dataroot /data/CUB_200_2011
python train_l2t_ww.py --dataset dog --datasplit dog --dataroot /data/dog
python train_l2t_ww.py --dataset cifar100 --datasplit cifar100 --dataroot /data/ --experiment logs/cifar100_0/ --source-path logs --source-model resnet32 --source-domain tinyimagenet-200 --target-model vgg9_bn --pairs 4-0,4-1,4-2,4-3,4-4,9-0,9-1,9-2,9-3,9-4,14-0,14-1,14-2,14-3,14-4 --batchSize 128
python train_l2t_ww.py --dataset stl10 --datasplit stl10 --dataroot /data/ --experiment logs/stl10_0/ --source-path logs --source-model resnet32 --source-domain tinyimagenet-200 --target-model vgg9_bn --pairs 4-0,4-1,4-2,4-3,4-4,9-0,9-1,9-2,9-3,9-4,14-0,14-1,14-2,14-3,14-4 --batchSize 128

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