ToAlign: Task-Oriented Alignment for Unsupervised Domain Adaptation
Authors: Guoqiang Wei, Cuiling Lan, Wenjun Zeng, Zhizheng Zhang, Zhibo Chen
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on various benchmarks (e.g., Offce-Home, Visda 2017, and Domain Net) under different domain adaptation settings demonstrate the effectiveness of To Align which helps achieve the state-of-the-art performance. |
| Researcher Affiliation | Collaboration | 1 University of Science and Technology of China 2 Microsoft Research Asia |
| Pseudocode | No | The paper describes the methods and procedures using text and equations, but no explicitly labeled 'Pseudocode' or 'Algorithm' block is provided. |
| Open Source Code | Yes | The code is publicly available at https://github.com/microsoft/UDA. |
| Open Datasets | Yes | We use two commonly used benchmark datasets (i.e., Offce-Home [60] and Vis DA 2017 [46]) for SUDA and a large-scale dataset Domain Net [43] for MUDA and SSDA. |
| Dataset Splits | No | The paper describes the use of labeled source and unlabeled target data for different adaptation settings (SUDA, MUDA, SSDA) and mentions specific dataset configurations (e.g., one-shot/three-shot for SSDA), but it does not provide explicit training, validation, and test dataset split percentages or sample counts to reproduce the data partitioning. |
| Hardware Specification | Yes | Table 4: Training complexity comparison (on GTX TITAN X GPU) in terms of computational time (of one iteration) and GPU memory for a mini-batch with batch size 32. |
| Software Dependencies | No | The paper describes the network architecture and training details but does not provide specific software dependency versions (e.g., Python, PyTorch, or library versions). |
| Experiment Setup | Yes | We use the Res Net-50 [21] pre-trained on Image Net [30] as the backbone for SUDA, while using Res Net-101 and Res Net-34 for MUDA and SSDA respectively. Following [64, 40, 12], the image classifer C is composed of one fully connected layer. The discriminator D consists of three fully connected layers with inserted dropout and Re LU layers. We follow [69] to take an annealing strategy η0 to set the learning rate η, i.e., ηt = , where p indicates the progress of training that increases (1+γp)τ linearly from 0 to 1, γ = 10, and τ = 0.75. The initial learning rate η0 is set to 1e 3, 3e 4, 3e 4, and 1e 3 for SUDA on Offce-Home, SUDA on Vis DA-2017, MSDA on Domain Net, and SSDA on Domain Net, respectively. |