Adversarial Reweighting for Partial Domain Adaptation
Authors: Xiang Gu, Xi Yu, yan yang, Jian Sun, Zongben Xu
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 {xianggu,ericayu,yangyan92}@stu.xjtu.edu.cn {jiansun,zbxu}@xjtu.edu.cn |
| 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. |