Collaborative Semantic Consistency Alignment for Blended-Target Domain Adaptation
Authors: Yuwu Lu, Xue Hu, Waikeung Wong, Haoyu Huang
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on several datasets show that CSCA achieves promising classification performance. Experiments Datasets We conduct experiments on four standard DA benchmarks: Office-31 (Saenko et al. 2010), Office-Home (Venkateswara et al. 2017), Image CLEF-DA (Caputo et al. 2014), and the very large scale Domain Net (Peng et al. 2019) (0.6 million images). |
| Researcher Affiliation | Academia | 1South China Normal University, Guangzhou, China 2Hong Kong Polytechnic University, Hong Kong, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 in the Supp. Mat. summarizes the training process of CSCA. |
| Open Source Code | Yes | Code https://github.com/xuehu365/CSCA |
| Open Datasets | Yes | We conduct experiments on four standard DA benchmarks: Office-31 (Saenko et al. 2010), Office-Home (Venkateswara et al. 2017), Image CLEF-DA (Caputo et al. 2014), and the very large scale Domain Net (Peng et al. 2019) (0.6 million images). |
| Dataset Splits | No | The paper describes how domains are split to form source and blended-target tasks (e.g., "A W/D"), but does not provide specific training/test/validation splits within these domains or for the overall datasets. |
| Hardware Specification | No | The paper mentions using ResNet-50 as a backbone but does not specify any hardware details (e.g., GPU/CPU models, memory amounts) used for running the experiments in the main text. |
| Software Dependencies | No | The paper mentions using ResNet-50 and various data augmentation techniques, but it does not specify software versions for any libraries, frameworks, or programming languages used (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We set the number of projections M = 256, as done in (Lee et al. 2019). to reduce the impact of unreliable pseudo-labels, the elements a about low-confidence pseudo-labels in A (the maximum likelihood of prediction is less than threshold τ1) are not optimized during training. τ2 is a scaling temperature. λe and λv are balance parameters. λp and λf are weighting parameters. Ltotal = Lcls + λswd Lswd + Lgraph + Lcon, where λswd is a trade-off parameter. Fig. 3 (a) shows that with balance parameters λe = 1.0 and λv = 0.1, our accuracy achieves its optimum. Fig. 3 (b) indicates that with λp = 0.2 and λf = 1.0, the model s performance reaches its peak. For the loss trade-off parameter λswd, we find that setting λswd = 1.0 yields the best results, as shown in Fig. 3 (c). |