Class Overwhelms: Mutual Conditional Blended-Target Domain Adaptation
Authors: Pengcheng Xu, Boyu Wang, Charles Ling
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments. We evaluate our method based on standard BTDA tasks... We summarize the standard BTDA in Table 1. Ablation and Analysis. |
| Researcher Affiliation | Academia | 1 Western University, London, ON N6A 5B7, Canada 2 Vector Institute, Toronto, ON M5G 1M1, Canada |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states 'We followed the implementations in (Junguang Jiang, Baixu Chen, Bo Fu, Mingsheng Long 2020)' which refers to baseline implementations, but does not provide a concrete access link or explicit statement for the source code of their own methodology. |
| Open Datasets | Yes | Datasets. We evaluate our method based on standard BTDA tasks (Chen et al. 2019; Roy et al. 2021a): Office-31 (Saenko et al. 2010), Office-Home (Venkateswara et al. 2017), Domain Net (Peng et al. 2019), and a specialized dataset Office Home-LMT for label shift in BTDA. |
| Dataset Splits | No | The paper states 'For evaluation, we use one domain as the source and the rest as blended targets' but does not provide specific training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for running its experiments. |
| Software Dependencies | No | The paper mentions 'SGD optimizer' but does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | For all datasets, we use SGD optimizer with learning rates η0 = 0.01, α = 10, and β = 0.75. We set the uncertainty threshold γ = 0.05 for all datasets. Since CST and SENTRY use Auto Augment for data augmentation, we set the number of transformations N = 1 and the transform severity M = 2.0 in Auto Augment (Lim et al. 2019) for a fair comparison. |