Adversarial Support Alignment
Authors: Shangyuan Tong, Timur Garipov, Yang Zhang, Shiyu Chang, Tommi S. Jaakkola
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We quantitatively evaluate the method across domain adaptation tasks with shifts in label distributions. Our experiments show that the proposed method is more robust against these shifts than other alignment-based baselines. |
| Researcher Affiliation | Collaboration | Shangyuan Tong MIT CSAIL Timur Garipov MIT CSAIL Yang Zhang MIT-IBM Watson AI Lab Shiyu Chang UC Santa Barbara Tommi Jaakkola MIT CSAIL |
| Pseudocode | Yes | Algorithm 1 Our proposed ASA algorithm. |
| Open Source Code | Yes | We provide the code reproducing experiment results at https://github.com/timgaripov/asa. |
| Open Datasets | Yes | We use USPS (Hull, 1994) and MNIST (Le Cun et al., 1998) datasets for this adaptation problem. We use STL (Coates et al., 2011) and CIFAR-10 (Krizhevsky, 2009) for this adaptation task. We use train and validation sets of the Vis DA-17 challenge (Peng et al., 2017). |
| Dataset Splits | Yes | We use train and validation sets of the Vis DA-17 challenge (Peng et al., 2017). |
| Hardware Specification | Yes | The computational experiments presented in this paper were performed on Satori cluster developed as a collaboration between MIT and IBM. |
| Software Dependencies | No | The paper mentions 'Weights & Biases (Biewald, 2020)' but does not specify a version number for this or any other software dependencies. |
| Experiment Setup | Yes | We train all methods for 65 000 steps with batch size 64. We train the feature extractor, the classifier, and the discriminator with SGD (learning rate 0.02, momentum 0.9, weight decay 5 10 4). |