Visual Feature Attribution using Wasserstein GANs
Implemented with Chainer
Requirements
Chainer, OpenCV
$ pip install chainer opencv-python
How to run
$ python vagan.py [options]
You can read help with -h
option.
$ python vagan.py -h
usage: vagan.py [-h] [-b BATCHSIZE] [-e EPOCH] [--alpha ALPHA] [--beta1 BETA1]
[--beta2 BETA2] [--n_cri1 N_CRI1] [--n_cri2 N_CRI2]
[--gp_lam GP_LAM] [--l1_lam L1_LAM] [-g G]
[--result_dir RESULT_DIR]
[--neg_numbers [NEG_NUMBERS [NEG_NUMBERS ...]]]
[--pos_numbers [POS_NUMBERS [POS_NUMBERS ...]]]
Visual Feature Attribution using Wasserstein GANs, CVPR 2018
optional arguments:
-h, --help show this help message and exit
-b BATCHSIZE, --batchsize BATCHSIZE
-e EPOCH, --epoch EPOCH
--alpha ALPHA alpha of Adam optimizer
--beta1 BETA1 beta1 of Adam
--beta2 BETA2 beta2 of Adam
--n_cri1 N_CRI1
--n_cri2 N_CRI2
--gp_lam GP_LAM weight of gradient penalty (WGAN-GP)
--l1_lam L1_LAM L1 loss of generator
-g G GPU ID (negative value indicates CPU mode)
--result_dir RESULT_DIR
--neg_numbers [NEG_NUMBERS [NEG_NUMBERS ...]]
digits regarded as negative example
--pos_numbers [POS_NUMBERS [POS_NUMBERS ...]]
digits regarded as positive example