Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Target Semantics Clustering via Text Representations for Robust Universal Domain Adaptation
Authors: Weinan He, Zilei Wang, Yixin Zhang
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, we evaluate the universality of Uni DA algorithms under four category shift scenarios. Extensive experiments on four benchmarks demonstrate the effectiveness and robustness of our method, which has achieved state-of-the-art performance. |
| Researcher Affiliation | Academia | 1University of Science and Technology of China, Hefei, China 2Institute of Artificial Intelligence, Hefei Comprehensive National Science Center |
| Pseudocode | No | The paper mentions summarizing a process in the Appendix for clarity and rigor, but no explicit pseudocode or algorithm block is present in the main text provided. |
| Open Source Code | Yes | Code https://github.com/Sapphire-356/TASC |
| Open Datasets | Yes | Our method will be validated on four popular datasets in Domain Adaptation, i.e., Office (Saenko et al. 2010), Office-Home (Venkateswara et al.), Vis DA (Peng et al. 2018), and Domain Net (Peng et al. 2019). |
| Dataset Splits | Yes | Detailed classes split in these scenarios are summarized in Appendix, which is the same as dataset split in (Qu et al. 2023). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using a pre-trained CLIP model with Vi T-B/16 and Transformer, and Lo RA, but does not provide specific version numbers for these or any other software libraries or frameworks. |
| Experiment Setup | Yes | Lo RA (Hu et al. 2021) is used in all transformer blocks in both image and text encoders with rank = 8. We adopt the same learning rate scheduler η = η0 (1 + 10 p) 0.75 as (Long et al.; Liang, Hu, and Feng), where p is the training progress changing from 0 to 1 and η0 = 0.0001. For the hyper-parameters, we empirically set the λdiv to 0.6,...τ is set to 0.02. In the discrete optimization step of TASC, nc = 300, γent = 0.3, and Nouter = 20. K0 is set to 100 for Office, Office-Home, and Vis DA, but 400 for Domain Net. |