Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
ToAlign: Task-Oriented Alignment for Unsupervised Domain Adaptation
Authors: Guoqiang Wei, Cuiling Lan, Wenjun Zeng, Zhizheng Zhang, Zhibo Chen
NeurIPS 2021 | Venue PDF | 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. |