CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation
Authors: Tongkun Xu, Weihua Chen, Pichao WANG, Fan Wang, Hao Li, Rong Jin
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our proposed method achieves the best performance on public UDA datasets, e.g. Vis DA-2017 and Domain Net. |
| Researcher Affiliation | Collaboration | Tongkun Xu12 , Weihua Chen1 , Pichao Wang1, Fan Wang1, Hao Li1, Rong Jin1 1Alibaba Group, 2Shandong University |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks are present in the paper. |
| Open Source Code | Yes | Code and models are available at https: //github.com/CDTrans/CDTrans. |
| Open Datasets | Yes | The proposed method is verified on four popular UDA benchmarks, including Vis DA-2017 (Peng et al., 2017), Office-Home (Venkateswara et al., 2017), Office-31 (Saenko et al., 2010) and Domain Net (Peng et al., 2019). |
| Dataset Splits | No | The paper mentions using well-known UDA benchmarks (Vis DA-2017, Office-Home, Office-31, Domain Net), which typically have predefined splits. However, it does not explicitly state the train/validation/test dataset splits (e.g., percentages or counts) within the paper's text. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions using Dei T-small and Dei T-base as backbones and Stochastic Gradient Descent, but does not provide specific version numbers for software dependencies like deep learning frameworks (e.g., PyTorch, TensorFlow) or programming languages. |
| Experiment Setup | Yes | The input image size in our experiments is 224 224. Both the Dei T-small and Dei T-base (Touvron et al., 2021) are adopted as our backbone for fair comparison. We use the Stochastic Gradient Descent algorithm with the momentum of 0.9 and weight decay ratio 1e-4 to optimize the training process. The learning rate is set to 3e-3 for Office-Home, Office-31 and Domain Net, 5e-5 for Vis DA-2017 since it can easily converge. The batch size is set to 64. |