Bayesian Domain Adaptation with Gaussian Mixture Domain-Indexing
Authors: Yanfang Ling, Jiyong Li, Lingbo Li, Shangsong Liang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We verify the effectiveness of GMDI via experimental comparison and analysis. In particular, we answer three research questions: (RQ1) Can the performance of GMDI for domain adaptation outperform baselines? (RQ2) How effective is the global domain indices inferred by GMDI? (RQ3) How does the number of mixture components K affect results? Additional experimental results are available in Appendix K. |
| Researcher Affiliation | Collaboration | Yanfang Ling Sun Yat-sen University lingyf3@mail2.sysu.edu.cn Jiyong Li Sun Yat-sen University lijy373@mail2.sysu.edu.cn Lingbo Li Inf Mind Technology Ltd lingbo@infmind.ai Shangsong Liang Sun Yat-sen University liangshangsong@gmail.com |
| Pseudocode | Yes | The procedure of our proposed model GMDI is summarized by the pesuedo codes in Algorithm 1. Let φ represent the parameters of the distribution p( ) in the generative process. Algorithm 1 Bayesian Domain Adaptation with Gaussian Mixture Domain-Indexing. |
| Open Source Code | Yes | Source code is publicly available from https://github.com/lingyf3/GMDI. |
| Open Datasets | Yes | Datasets. We compare GMDI with existing DA methods on the following datasets (see Appendix H and Appendix I for more details): Circle [36] is used for binary classification task. DG-15 and DG60 [40] are synthetic datasets used for binary classification task. TPT-48 [40] dataset is a real-world dataset used for regression task. Comp Cars [43] dataset is a real-world dataset for 4-way classification task. |
| Dataset Splits | No | The paper defines source and target domains for training and testing, but it does not specify explicit numerical splits for training, validation, and testing sets (e.g., 80/10/10 percentages or exact sample counts for each split). |
| Hardware Specification | Yes | We run experiments on a single machine using 1 NVIDIA Ge Force RTX 2080Ti with 11GB memory, 56 Intel Xeon CPUs (E5-2680 v4 @ 2.40GHz). |
| Software Dependencies | No | The paper mentions implementing the model based on VDI's code but does not specify software dependencies with version numbers (e.g., Python, PyTorch/TensorFlow versions). |
| Experiment Setup | Yes | We set the maximum number of mixture components K from 2,3, and the concentration parameter α to 1 throughout the experiments. Except for DG-15 and DG-60 datasets, which have a batch size of 32, all other datasets use a batch size of 16. Our model is trained with 20 to 100 warmup steps, learning rates ranging from 1 10 5 to 1 10 3, and λ ranging from 0.1 to 1. |