Invertible Projection and Conditional Alignment for Multi-Source Blended-Target Domain Adaptation
Authors: Yuwu Lu, Haoyu Huang, Waikeung Wong, Xue Hu
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiment results on the Image CLEF-DA, Office-Home, and Domain Net datasets validate the effectiveness of our method. |
| Researcher Affiliation | Academia | Yuwu Lu1, 2, Haoyu Huang1, Waikeung Wong2*, Xue Hu1 1South China Normal University, Guangzhou, China 2Hong Kong Polytechnic University, Hong Kong, China EMAIL, EMAIL |
| Pseudocode | No | The paper describes the method using mathematical equations and prose, but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code https://github.com/hyhuang99/IPCA |
| Open Datasets | Yes | We evaluated and compared the state-of-the-art (SOTA) methods with our method on three popular DA datasets (i.e., Domain Net, Office-Home, and Image CLEF-DA). The Domain Net (Peng et al. 2019) is the largest dataset in DA that contains 0.6 million images from 345 categories in 6 domains: Clipart (c), Infograph (i), Painting (p), Quickdraw (q), Real-world (r), and Sketch (s). ... The Office-Home (Venkateswara et al. 2017) is a challenge dataset with label imbalance, which contains 15,500 images in total. ... The Image CLEF-DA (Caputo et al. 2014) contains a total of 2,400 images, including 12 common categories in 4 domains: Bing (B), Caltech (C), Image Net (I), and Pascal (P). |
| Dataset Splits | No | To highlight the challenge in MBDA setting, we cannot anymore use the standard protocols from the above three datasets. Thus, for SSDA setting, one column denotes one SSDA task, e.g., r c in Table 1a. For MSDA setting, two domains are selected as sources and one domain is selected as target, e.g., r+s c and r+s p in Table 1a. For MTDA/BTDA setting, one domain is selected as source and two domains are selected as targets, e.g., r c+p and s c+p in Table 1a. For MMDA/MBDA setting, two domains are sources, and the other domains are targets, e.g., r+s c+p in Table 1a. |
| Hardware Specification | Yes | all experiments on three datasets utilize the same backbone network, Res Net-50 (He et al. 2016), and run on a Nvidia Ge Force RTX-4090 GPU. |
| Software Dependencies | Yes | We implemented and evaluated our method on the Py Torch (Paszke et al. 2019) platform; the number of Py Torch is 1.13.1. ... The version of CUDA is 11.7. |
| Experiment Setup | Yes | The number of INN blocks which contains in the IPM is K = 5. ... The batch size of all experiments in the training step is set to 32. The optimizer is Stochastic Gradient Descent (SGD) with a momentum parameter of 0.9 and a weight decay of 1e-3. In addition, the learning rate is set to 1e-3 and updated by the Lambda LR (Paszke et al. 2019) during the training process. |