The document proposes a self-domain adaptation framework that utilizes unlabeled target data at inference time. It introduces meta-learning based adaptor learning for training, where the adaptor is initialized using multiple source domains and meta-learning. During testing, the adaptor is further trained using only the unlabeled target data while keeping other parameters fixed, allowing adaptation to the target domain. The conclusions are that this approach introduces effective adaptor learning and loss functions under unsupervised learning conditions.

1. 발표자 – Vision AI 김민하

2. Introduction
DA의 단점 (target label을 알아야함)과 DG의 단점(unseen target data는 활용하지
않음)을 지적하며, unseen target data를 활용하여 domain adaptive한 학습방법을
제안함 → novelty
저자들은 Inference time에서의 Target data를 이용한 self-domain adaptation
framework를 제안
Contribution: Adaptor learning based on Meta-learning
Training → . Multiple source domain + Meta-learning 기반의 Adaptor learning
을 적용함
Test → Unlabeled target dataset을 이용해 Adaptor 학습

3. Comparison with DG and DA

4. r

5. Meta train on Train-samples

6. Meta train
Meta train on Valid-samples
To prevent the feature mode collapse

7. Meta Test on Valid-samples

8. Adapt Step - only train ‘Adaptor’
Given the well-initialized adaptor,
during testing, we first optimize the
adaptor using only the unlabeled
test domain data with all the other
parameters fixed using Equation 7.

9. Experiments

10. Experiments

11. ● Finally, their conclusions are..
● This paper introduces meta-learning-based adaptor learning for adaptor
initialization.
● This paper introduces effective adaptor loss to learn the adaptor under
unsupervised learning.
Conclusion

12. Opinion..
● Unlabelled Target dataset이 존재한다는 조건하에 진행되는 방식
● 예측하고싶은 target dataset이 있을 때 마다 새로 학습해야 하는지
● RGB x HSV channel → 6 channel 학습 : 공평한 실험조건인지
● 여러개 target domain도 가능할지