Code for CVPR2021 "Visualizing Adapted Knowledge in Domain Transfer". Visualization for domain adaptation. #explainable-ai

Last update: Dec 25, 2022

Overview

Visualizing Adapted Knowledge in Domain Transfer

@inproceedings{hou2021visualizing,
  title={Visualizing Adapted Knowledge in Domain Transfer},
  author={Hou, Yunzhong and Zheng, Liang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2021}
}

Under construction

Overview

This repo dedicates to visualize the learned knowledge in domain adaptation. To understand the adaptation process, we portray the knowledge difference between the source and target model with image translation, using the source-free image translation (SFIT) method proposed in our CVPR2021 paper Visualizing Adapted Knowledge in Domain Transfer.

Specifically, we feed the generated source-style image to the source model, and the original target image to the target model, formulating two branches respectively. Through update the generated image, we force similar outputs between the two branches. When such requirements are met, the image difference should compensate for and can represent the knowledge difference between models.

Content

Dependencies
Data Preparation
Run the Code
- Train source and target models
- Visualization

Dependencies

This code uses the following libraries

python 3.7+
pytorch 1.6+ & torchvision
numpy
matplotlib
pillow
scikit-learn

Data Preparation

By default, all datasets are in ~/Data/. We use digits (automatically downloaded), Office-31, and VisDA datasets.

Your ~/Data/ folder should look like this

Data
├── digits/
│   └── ...
├── office31/ 
│   └── ...
└── visda/
    └── ...

Run the Code

Train source and target models

Once the data preparation is finished, you can train source and target models using unsupervised domain adaptation (UDA) methods

python train_DA.py -d digits --source svhn --target mnist

Currently, we support MMD --da_setting mmd, ADDA --da_setting adda, and SHOT --da_setting shot.

Visualization

Based on the trained source and target models, we visualize their knowledge difference via SFIT

python train_SFIT.py -d digits --source svhn --target mnist

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Comments

da_setting adda seems no effect

python train_DA.py -d digits --source svhn --target mnist --da_setting adda

Training target model... Train Epoch: 22, Batch:937, S: [c: 0.000, d: 1.354], Time: 12.886 Testing target model on [source]... Test, loss: 4.267, prec: 6.4%, Time: 2.465 Testing target model on [target]... Test, loss: 4.293, prec: 9.8%, Time: 1.192 73%|████████████████████████████████████████████████████████████████████████████████████▎ | 22/30 [06:11<02:15, 16.93s/it]Training target model... Train Epoch: 23, Batch:937, S: [c: 0.000, d: 1.429], Time: 12.536 Testing target model on [source]... Test, loss: 6.942, prec: 7.8%, Time: 2.410 Testing target model on [target]... Test, loss: 7.577, prec: 10.3%, Time: 1.235 77%|████████████████████████████████████████████████████████████████████████████████████████▏ | 23/30 [06:27<01:57, 16.75s/it]Training target model... Train Epoch: 24, Batch:937, S: [c: 0.000, d: 1.375], Time: 13.038 Testing target model on [source]... Test, loss: 14.831, prec: 9.7%, Time: 2.508 Testing target model on [target]... Test, loss: 15.324, prec: 9.8%, Time: 1.543 80%|████████████████████████████████████████████████████████████████████████████████████████████ | 24/30 [06:44<01:41, 16.91s/it]Training target model... Train Epoch: 25, Batch:937, S: [c: 0.000, d: 1.367], Time: 12.633 Testing target model on [source]... Test, loss: 6.635, prec: 6.1%, Time: 2.474 Testing target model on [target]... Test, loss: 7.577, prec: 10.1%, Time: 1.280 83%|███████████████████████████████████████████████████████████████████████████████████████████████▊ | 25/30 [07:01<01:23, 16.80s/it]Training target model... Train Epoch: 26, Batch:937, S: [c: 0.000, d: 1.390], Time: 12.824 Testing target model on [source]... Test, loss: 9.628, prec: 15.9%, Time: 2.390 Testing target model on [target]... Test, loss: 10.641, prec: 10.3%, Time: 1.202 87%|███████████████████████████████████████████████████████████████████████████████████████████████████▋ | 26/30 [07:17<01:06, 16.73s/it]Training target model... Train Epoch: 27, Batch:937, S: [c: 0.000, d: 1.447], Time: 12.648 Testing target model on [source]... Test, loss: 8.766, prec: 11.1%, Time: 3.055 Testing target model on [target]... Test, loss: 8.690, prec: 10.1%, Time: 1.376

.. bad effect, any problems?

opened by xiaoyuan1996 1
target model optimizer in DA

optimizer_T in train_DA.py of 'SHOT' method only optimizes net_T.base.parameters() without net_T.bottleneck ,but function train_net_T() in da_trainer.py sets self.net_T.bottleneck.train() mode. Don't they conflicts with each other?

opened by chenxi52 1
custom dataset

Hello, thanks for your work. I want to know if I can use my own dataset training model?What would my dataset look like if I could?Thanks for your kind attention and look forward your prompt reply.

opened by zmj876902008 1

Owner

Yunzhong Hou

Yunzhong Hou, a PhD student at ANU.

GitHub

Code for CVPR2021 "Visualizing Adapted Knowledge in Domain Transfer". Visualization for domain adaptation. #explainable-ai

Related tags

Overview

Visualizing Adapted Knowledge in Domain Transfer

Under construction

Overview

Content

Dependencies

Data Preparation

Run the Code

Train source and target models

Visualization

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