mil-tokyo / dg_mmld Goto Github PK

Hi,

I wanted to try your method on the VLCS dataset, as described in your paper, but I am having a hard time finding the dataset. Can you point me to where you get the dataset?

Thanks!

I don't know what is the size of pseudo domain label.Where is the corrsponding code? Thank you

Hi ! Thanks for sharing the work! But the link of PACS Dataset failed. Could you update the download link of PACS! Thank u

HI @milhidaka I am getting the following error
Traceback (most recent call last):
File "../main/main.py", line 90, in
num_classes=source_train.dataset.dataset.num_class, num_domains=disc_dim, pretrained=True)
File "../util/util.py", line 72, in get_network_fn
return nets_map[name]train
File "../model/caffenet.py", line 66, in caffenet
state_dict = torch.load("alexnet_caffe.pth.tar", map_location=lambda storage, loc: storage, pickle_module=pickle)
File "/home/vinodkk/miniconda2/envs/dg/lib/python3.6/site-packages/torch/serialization.py", line 358, in load
return _load(f, map_location, pickle_module)
File "/home/vinodkk/miniconda2/envs/dg/lib/python3.6/site-packages/torch/serialization.py", line 532, in _load
magic_number = pickle_module.load(f)
_pickle.UnpicklingError: invalid load key, '<'.
PACS/default/art1
path ../../../../../dataset/result/dg_mmld/PACS/default/art1

Source domain: photo, cartoon, sketch, Target domain: art_painting
Train: 7148, Val: 795, Test: 2048
args.model caffenet

Thanks for making your code public. I had a question about the loss function mentioned in the paper versus what has been implemented in the code:

In the paper you have eq.

However, in the implementation I find the following:

I am trying to understand how to correlate the two. I would appreciate it if you could provide more details on how the implementation matches the equation in the paper.