Awesome
XSR-OSDA
:fire: Implementation for the ''Interpretable Novel Target Discovery Through Open-Set Domain Adaptation (XSR-OSDA)'' work (under review).
XSR-OSDA is an extension work of the "SR-OSDA" paper published in ICCV 2021 [paper][Github].
Data Preparation
- I->AwA: 3D2 and AwA2
- DomainNet -> AwA: DomainNet & AwA2
- DomainNet -> LAD: DomainNet & LAD
| Dataset | Domain | Role | #Images | #Attributes | #Classes |
|---|---|---|---|---|---|
| DomainNet $\rightarrow$ AwA2 | AwA <br> Paint <br> Real | S / T | 9,343 / 15,306 <br> 3,441 / 5,760 <br> 5,251 / 10,047 | 85 | 10 / 17 |
| I $\rightarrow$ AwA | I / AwA | S / T | 2,970 / 37,322 | 85 | 40 / 50 |
| Domain $\rightarrow$ LAD | LAD <br> Paint <br> Real | S / T | 13,322 / 19,744 <br> 11,714 / 15,311 <br> 22,395 / 31,066 | 253 | 40 / 56 |
Dependencies
- Python 3.8
- Pytorch 1.10
Training
python main.py
Evaluation
- Open-set Domain Adaptation Task
$OS^*$: class-wise average accuracy on the seen categories.
$OS^\diamond$: class-wise average accuracy on the unseen categories correctly classified as "unknown".
$OS$: $\frac{OS^* \times C_{shr} + OS^\diamond}{C_{shr} + 1}$
$OS^{H}$: $\frac{ 2 \times OS^* \times OS^\diamond}{OS^* + OS^\diamond}$
- Semantic-Recovery Open-Set Domain Adaptation Task
$S$: class-wise average accuracy on shared classes
$U$: class-wise average accuracy on unknown classes
$H = \frac{2 \times S \times U}{ S + U}$
Citation
If you think this work is interesting, please cite:
@InProceedings{Jing_2021_XSROSDA,
author = {Jing, Taotao and Xia, Haifeng and Liu, Hongfu and Ding, Zhengming},
title = {Interpretable Novel Target Discovery Through Open-Set Domain Adaptation},
booktitle = {},
year = {}
}
Contact
If you have any questions about this work, feel free to contact