Proxy Denoising for Source-Free Domain Adaptation
Authors: Song Tang, Wenxin Su, Yan Gan, Mao Ye, Jianwei Dr. Zhang, Xiatian Zhu
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTS Datasets We evaluate four widely used domain adaptation benchmarks. Among them, Office31 (Saenko et al., 2010) and Office-Home (Venkateswara et al., 2017) are small-scaled and medium-scale datasets, respectively, whilst Vis DA (Peng et al., 2017) and Domain Net-126 (Saito et al., 2019) are both challenging large-scale datasets. Their details are provided in Appendix C. Settings We consider a variety of SFDA settings: (1) closed-set, (2) partial-set (initialized in SHOT (Liang et al., 2020)), (3) open-set (initialized in SHOT (Liang et al., 2020)), (4) generalized SFDA (Yang et al., 2021b), (5) multi-target (SF-MTDA, detailed in (Kumar et al., 2023)), (6) multisource (SF-MSDA, detailed in (Ahmed et al., 2021)), and (7) test-time adaptation (TTA) (Wang et al., 2021a). More details are given in Appendix D. |
| Researcher Affiliation | Collaboration | Song Tang1,2,3, Wenxin Su1, Yan Gan4, Mao Ye5,*, Jianwei Zhang2 & Xiatian Zhu6, 1University of Shanghai for Science and Technology, 2Universität Hamburg, 3Com Origin Mat Inc. 4Chongqing University, 5University of Electronic Science and Technology of China, 6University of Surrey |
| Pseudocode | Yes | Algorithm 1 Training of Pro De Input: Source model θs, Vi L model θv, target dataset XT , C prompts with context v, #iteration M. Procedure: 1: Initialisation: Set target model θt = θs, prompt context v ="a photo of a". 2: for m = 1:M do 3: Sample a batch X b T from XT . 4: Forward updated prompts and X b T through θv. 5: Forward X b T through θt. 6: Conduct proxy denoising for the Vi L predictions of X b T (Eq. (5)). 7: Update model θt and prompt context v by optimizing objective LPro De (Eq. (6)). 8: end for 9: return Adapted target model θt. |
| Open Source Code | Yes | Our code and data are available at https://github.com/tntek/ source-free-domain-adaptation. |
| Open Datasets | Yes | Datasets We evaluate four widely used domain adaptation benchmarks. Among them, Office31 (Saenko et al., 2010) and Office-Home (Venkateswara et al., 2017) are small-scaled and medium-scale datasets, respectively, whilst Vis DA (Peng et al., 2017) and Domain Net-126 (Saito et al., 2019) are both challenging large-scale datasets. |
| Dataset Splits | Yes | The source dataset is divided into the training set and testing set in a 0.9:0.1 ratio. |
| Hardware Specification | No | All experiments are conducted with Py Torch on a single GPU of NVIDIA RTX. |
| Software Dependencies | No | All experiments are conducted with Py Torch on a single GPU of NVIDIA RTX. The paper mentions PyTorch but does not specify a version number. |
| Experiment Setup | Yes | Hyper-parameter setting. The Pro De model involves four parameters: The correction strength factor ω in Eq. (5) and two trade-off parameters α, β and γ in objective LP ro De (Eq. (6)). On all four datasets, we set (ω, α, β) = (1, 1, 0.4). Parameter γ is sensitive to the dataset scale, also noted in the TPDS method (Tang et al., 2024a). In practice, the setting of γ = 1.0/1.0/0.1/0.5 is employed on Office-31, Office-Home, Vis DA and Domain Net-126, respectively. Training setting. We chose a batch size of 64 and utilized the SGD optimizer with a momentum of 0.9 and 15 training epochs on all datasets. |