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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation
Authors: Uiwon Hwang, Jonghyun Lee, Juhyeon Shin, Sungroh Yoon
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We performed experiments on challenging benchmark datasets, including Vis DA (Peng et al., 2017), Domain Net (Peng et al., 2019), Point DA (Qin et al., 2019), and Vis DA-RSUT (Li et al., 2021). We verified that our method outperforms existing state-of-the-art methods on 2D image, 3D point cloud, and highly imbalanced datasets. |
| Researcher Affiliation | Academia | Uiwon Hwang1 Jonghyun Lee2 Juhyeon Shin3 Sungroh Yoon2,3, 1 Division of Digital Healthcare, Yonsei University 2 Department of Electrical and Computer Engineering, Seoul National University 3 Interdisciplinary Program in Artificial Intelligence, Seoul National University |
| Pseudocode | Yes | Algorithm 1 Adaptation procedure of SF(DA)2 |
| Open Source Code | Yes | Code is available in Supplementary Material. |
| Open Datasets | Yes | In this section, we evaluate the performance of SF(DA)2 on several benchmark datasets: Office31 (Saenko et al., 2010), Vis DA (Peng et al., 2017), Domain Net (Peng et al., 2019), Point DA10 (Qin et al., 2019), and Vis DA-RSUT (Li et al., 2021). |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits (e.g., percentages or counts) needed to reproduce the experiment, beyond mentioning the total training and test images for Point DA-10 as part of the dataset description. |
| Hardware Specification | Yes | Experiments are conducted on a NVIDIA A40. |
| Software Dependencies | No | The paper mentions network architectures (Res Net-50, Res Net-101, Point Net) and optimizers (SGD, Adam) but does not provide specific version numbers for software libraries or frameworks like Python, PyTorch, or TensorFlow. |
| Experiment Setup | Yes | Most hyperparameters of our method do not require heavy tuning. We set K to 5 on Vis DA, Point DA-10, and Vis DA-RSUT, and 2 on Domain Net. We set α1 to 1e-4 on Vis DA, Domain Net, and Point DA-10, and 1e-3 on Vis DA-RSUT. We set α2 to 10 on Vis DA, Point Net-10, and Vis DA-RSUT, and 1 on Domain Net. We adopt SGD with momentum 0.9 and train 15 epochs for Vis DA, Domain Net, and Vis DA-RSUT. We adopt Adam (Kingma & Ba, 2014) and train 100 epochs for Point DA-10. We set batch size to 64 except for Domain Net, where we set it to 128 for a fair comparison. We set the learning rate for Vis DA and Vis DA-RSUT to 1e-4, 5e-5 for Domain Net, and 1e-6 for Point DA-10, except for the last two layers. Learning rates for the last two layers are increased by a factor of 10, except for Point Net-10 where they are increased by a factor of 2 following NRC (Yang et al., 2021a). |