Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation
Authors: Xinyang Chen, Sinan Wang, Mingsheng Long, Jianmin Wang
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that the approach significantly improves upon representative adversarial domain adaptation methods to yield state of the art results. (Abstract) and tables like Table 1. Accuracy (%) on Office-31 for unsupervised domain adaptation (Res Net-50). |
| Researcher Affiliation | Academia | Xinyang Chen 1 Sinan Wang 1 Mingsheng Long 1 Jianmin Wang 1 1School of Software, BNRist, Research Center for Big Data, Tsinghua University. E-mail: chenxiny17@mails.tsinghua.edu.cn. Correspondence to: M. Long <mingsheng@tsinghua.edu.cn>. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code of BSP is available at github.com/thuml/ Batch-Spectral-Penalization. |
| Open Datasets | Yes | Office-31 (Saenko et al., 2010) is a vanilla dataset for visual domain adaptation with 4,652 images in 31 categories from three domains: Amazon (A), Webcam (W) and DSLR (D). Office-Home (Venkateswara et al., 2017) is a more difficult dataset than Office-31, which consists of around 15,500 images from 65 classes in office and home settings... Vis DA-2017 (Peng et al., 2017) is a challenging simulation-to-real dataset... Digits (Ganin et al., 2016). We use three digits datasets: MNIST, USPS, and SVHN. |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits or cross-validation setup needed to reproduce the experiment beyond stating that all labeled source and unlabeled target samples participate in training. |
| Hardware Specification | Yes | In addition, in each min-batch iteration, (c) uses 0.342s while (d) uses 0.359s (Titan V), which shows that SVD does not cost much computation for feature matrix of a small batch. |
| Software Dependencies | No | The paper mentions 'Py Torch' as the implementation framework but does not specify its version number or versions of other key software components. |
| Experiment Setup | Yes | We fix δ = 1 and β = 10 4 in all experiments. The learning rates of the layers trained from scratch are set to be 10 times those of fine-tuned layers. We adopt mini-batch SGD with momentum of 0.95 using the learning rate and progressive training strategies of DANN (Ganin & Lempitsky, 2015). |