Description
Task Description – Domain Adaptation
- Imagine you want to do tasks related to the 3D environment, and then discover that…
○ 3D images are difficult to mark and therefore expensive.
○ Simulated images (such as simulated scene on GTA-5) are easy to label.
Why not just train on simulated images?
Task Description – Domain Adaptation
| Net (D) |
| Net (U) |
| Feat A |
| Output |
| ???? |
| ???? |
- For Net, the input is “abnormal”, which makes Net doesn’t work properly.
???
Task Description – Domain Adaptation
| Net (D) |
| Net (U) |
| Output |
| Output |
- Therefore, one simple way to solve this problem is to make the distributions of FeatA and FeatB similar.
similar
Task Description – Domain Adaptation
- Our task: Given real images (with labels) and drawing images (without labels), please use domain adaptation technique to make your network predict the drawing images correctly.
Dataset
- Label: 10 classes (numbered from 0 to 9), as following pictures discribed.
- Training : 5000 (32, 32) RGB real images (with label). Testing : 100000 (28, 28) gray scale drawing images.
Data Format
- Unzip zip, the data format is as below: ● real_or_drawing/
○ train_data/
■ 0/
- bmp, 1.bmp … 499.bmp
■ 1/
- bmp, 501.bmp … 999.bmp
■ … 9/
○ test_data/
■ 0/
- bmp
- bmp
- … 99999.bmp
Data Format
- You can simply use the following code to get dataloader after extracting the zip. (You can apply your own source/target transform function.)
source_dataset = ImageFolder(‘real_or_drawing/train_data’, transform=source_transform) target_dataset = ImageFolder(‘real_or_drawing/test_data’, transform=target_transform)
source_dataloader = DataLoader(source_dataset, batch_size=32, shuffle=True) target_dataloader = DataLoader(target_dataset, batch_size=32, shuffle=True) test_dataloader = DataLoader(target_dataset, batch_size=1
