Information-Theoretic Analysis of Unsupervised Domain Adaptation
Authors: Ziqiao Wang, Yongyi Mao
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This paper uses information-theoretic tools to analyze the generalization error in unsupervised domain adaptation (UDA). We present novel upper bounds for two notions of generalization errors. [...] Specifically, we present two simple techniques for improving generalization in UDA and validate them experimentally. [...] Experiments are performed to verify the effectiveness of these strategies. |
| Researcher Affiliation | Academia | Ziqiao Wang & Yongyi Mao University of Ottawa {zwang286,ymao}@uottawa.ca |
| Pseudocode | Yes | Algorithm 1 Controlling Label Information (Appendix D.2, page 28) |
| Open Source Code | No | Appendix D states: 'Our code builds largely on the implementation from Gulrajani & Lopez-Paz (2021)3 and Nguyen et al. (2022)4.' Footnotes 3 and 4 provide GitHub links to these existing external implementations. The paper does not explicitly state that *their* specific modifications or code for this paper are released or available. |
| Open Datasets | Yes | Datasets We select two popular small datasets, Rotated MNIST and Digits, to compare the different methods. Rotated MNIST is built based on the MNIST dataset (Le Cun et al., 2010) [...] Digits consists of three sub-datasets, namely MNIST, USPS (Hull, 1994) and SVHN (Netzer et al., 2011) |
| Dataset Splits | Yes | Every dataset has a validation set, and the model selection scheme is based on the best performance achieved on the validation set of target domain during training (oracle). |
| Hardware Specification | Yes | We implemented our approach using Py Torch (Paszke et al., 2019) and conducted all experiments on NVIDIA Tesla V100 GPUs with 32 GB of memory. |
| Software Dependencies | No | The paper mentions 'Py Torch (Paszke et al., 2019)' which implies a version from the citation year, but it does not explicitly state a specific version number (e.g., PyTorch 1.9). Python is also mentioned without a version. |
| Experiment Setup | Yes | Other settings are also the same as Gulrajani & Lopez-Paz (2021) and Nguyen et al. (2022), for example, each algorithm is trained for 100 epochs. To select the hyperparameters (λ1 and λ2) for ERM-GP, ERM-KL, KL-GP and KL-CL, we perform random search. Specifically, λ1 is searched between [0.1, 0.9] and λ2 is searched between [10 6, 0.8]. |