Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers
Authors: Hong Liu, Mingsheng Long, Jianmin Wang, Michael Jordan
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A series of experiments validate that our approach advances the state of the arts on a variety of domain adaptation tasks in vision and NLP, including object recognition, learning from synthetic to real data, and sentiment classification. |
| Researcher Affiliation | Academia | Hong Liu 1 2 Mingsheng Long 1 3 Jianmin Wang 1 3 Michael I. Jordan 4 1School of Software 2Department of Electronic Engineering 3BNRist, Research Center for Big Data, Tsinghua University, Beijing, China 4University of California, Berkeley, USA. |
| Pseudocode | Yes | We summarize the detailed training procedure in Algorithm 1. TAT runs over the feature-level examples f and propagates only through the deep classifier C (usually of no more than three layers), which is very computationally efficient (an order of magnitude faster than feature adaptation methods). |
| Open Source Code | Yes | 5. Experiments We evaluate TAT on five domain adaptation datasets. Codes and datasets are made available at github.com/thuml/ Transferable-Adversarial-Training. |
| Open Datasets | Yes | 5. Experiments We evaluate TAT on five domain adaptation datasets. Codes and datasets are made available at github.com/thuml/ Transferable-Adversarial-Training. |
| Dataset Splits | Yes | We use reverse validation for hyperparameter selection (Zhong et al., 2010). |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or memory used for running experiments. |
| Software Dependencies | No | The paper mentions using Adam, ResNet-50, and scikit-learn, but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | For image datasets, we use Res Net-50 (He et al., 2016) pretrained on Image Net (Russakovsky et al., 2015) to extract original feature representations. We use Adam (Kingma & Ba, 2014) with initial learning rate η0 = 10 4. We adopt the inverse-decay strategy of DANN (Ganin et al., 2016), where the learning rate changes by ηp = η0 (1+ωp)φ , ω = 10, φ = 0.75, and p is the progress ranging from 0 to 1. For image datasets, β = 5 and γ = 1. |