Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Adversarial Reweighting for Partial Domain Adaptation
Authors: Xiang Gu, Xi Yu, yan yang, Jian Sun, Zongben Xu
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 22,960 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,009 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our method achieves state-of-the-art results on the benchmarks of Image Net-Caltech, Office-Home, Vis DA-2017, and Domain Net. Ablation studies also confirm the effectiveness of our approach. |
| Researcher Affiliation | Academia | Xiang Gu, Xi Yu, Yan Yang, Jian Sun , and Zongben Xu School of Mathematics and Statistics, Xi an Jiaotong University, P.R. China EMAIL EMAIL |
| Pseudocode | Yes | We give the pseudo-code of the training algorithm in Supp. D. |
| Open Source Code | Yes | Our code is available at https://github.com/XJTU-XGU/ Adversarial-Reweighting-for-Partial-Domain-Adaptation. |
| Open Datasets | Yes | Office-31 dataset [36] contains 4,652 images of 31 categories, collected from three domains: Amazon (A), DSLR (D), and Webcam (W). ... Image Net-Caltech is built with Image Net (I) [35] and Caltech-256 (C) [12]... Office-Home [43] consists of four domains... Vis DA-2017 [32] is a large-scale challenging dataset... Domain Net [31] is another large-scale challenging dataset... |
| Dataset Splits | No | The paper describes how target domains are built (e.g., 'We use the first 6 classes in alphabetical order as the target domain' for Vis DA-2017) but does not provide explicit train/validation/test dataset split percentages or sample counts for the overall experimental setup. |
| Hardware Specification | Yes | We implement our method using Pytorch [30] on a Nvidia Tesla v100 GPU. |
| Software Dependencies | No | The paper mentions 'Pytorch [30]' and 'CVXPY [7] package' but does not specify their version numbers. |
| Experiment Setup | Yes | We use the SGD algorithm with momentum 0.9 to update θF and θC. The learning rate of θC is ten times that of θF . θD is updated by the Adam [18] algorithm with learning rate 0.001. Following [8], we adjust the learning rate η of θC by η = 0.01 (1+10p) 0.75 , where p is the training progress linearly changing from 0 to 1. We set the batchsize to 36, M = 500, and N = 36M. |