Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation
Authors: JoonHo Jang, Byeonghu Na, Dong Hyeok Shin, Mingi Ji, Kyungwoo Song, Il-chul Moon
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we evaluate UADAL on the benchmark datasets, which shows that UADAL outperforms other methods with better feature alignments by reporting state-of-the-art performances. |
| Researcher Affiliation | Collaboration | Joon Ho Jang KAIST adkto8093@kaist.ac.kr Byeonghu Na KAIST wp03052@kaist.ac.kr Dong Hyeok Shin KAIST tlsehdgur0@kaist.ac.kr Mingi Ji KAIST qwertgfdcvb@kaist.ac.kr now at Google (mingiji@google.com) Kyungwoo Song University of Seoul kyungwoo.song@uos.ac.kr Il-Chul Moon KAIST, Summary.AI icmoon@kaist.ac.kr |
| Pseudocode | Yes | Algorithm 1 in Appendix B.3.1 enumerates the detailed training procedures of UADAL. |
| Open Source Code | Yes | The code will be publicly available on https://github.com/JoonHo-Jang/UADAL. |
| Open Datasets | Yes | We utilized several benchmark datasets. Office-31 [22] consists of three domains: Amazon (A), Webcam (W), and DSLR (D) with 31 classes. Office-Home [30] is a more challenging dataset with four different domains: Artistic (A), Clipart (C), Product (P), and Real-World (R), containing 65 classes. Vis DA [21] is a large-scale dataset from synthetic images to real one, with 12 classes. (In the self-assessment, point 4d states: 'We mentioned that we use the public benchmark dataset, in Section 4.1.') |
| Dataset Splits | Yes | In terms of the class settings, we follow the experimental protocols by [23]. (In the self-assessment, point 3b states: 'Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes]') This implies that data splits, including those for validation, are specified in the paper or supplementary materials. |
| Hardware Specification | Yes | All the experiments were conducted on a single GPU (NVIDIA A100). (In the self-assessment, point 3d states: 'Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] The details in Appendix C.1.1.') |
| Software Dependencies | No | Our implementation is based on the PyTorch framework [16]. The paper mentions PyTorch but does not provide a specific version number or details for other software dependencies with version numbers. |
| Experiment Setup | Yes | For the training, we use SGD optimizer with momentum 0.9, learning rate initialized by 0.01, and decay by 0.1 at 80% and 90% of total training iterations. Batch size is set to 32. The total iterations is 10,000 for Office-31 and 20,000 for Office-Home and VisDA. The initial fitting of Eq. (20) trains G and E for 500 iterations. |