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..
Style Adaptation and Uncertainty Estimation for Multi-Source Blended-Target Domain Adaptation
Authors: Yuwu Lu, Haoyu Huang, Xue Hu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on several challenging DA benchmarks, including the Image CLEF-DA, Office-Home, Vis DA 2017, and Domain Net datasets, demonstrate the superiority of our method over the state-of-the-art (SOTA) approaches. |
| Researcher Affiliation | Academia | Yuwu Lu , Haoyu Huang, and Xue Hu School of Artificial Intelligence, South China Normal University EMAIL |
| Pseudocode | Yes | Algorithm 1 SAUE for MBDA |
| Open Source Code | Yes | The source code of SAUE is provide in the Supplementary Material. |
| Open Datasets | Yes | Four standard benchmark datasets are used to validate the effectiveness of our proposed method. The Image CLEF-DA [40]... The Office-Home [41]... The Domain Net [14]... The Vis DA 2017 [42] dataset... |
| Dataset Splits | No | The paper mentions training data and test data implicitly through its use of benchmarks, but it does not specify a distinct validation set split or how it was used beyond 'training process'. |
| Hardware Specification | Yes | All experiments are run on a single Ge Force RTX-4090 GPU, and the batch size of both the source and blended-target domains are set to 32. |
| Software Dependencies | Yes | We utilize Py Torch framework [43] to perform our experiments; the Py Torch version is 1.13.1 and CUDA version is 11.7. |
| Experiment Setup | Yes | The optimizer is Stochastic Gradient Descent (SGD) with a momentum parameter of 0.9 and a weight decay of 1e-3. The learning rate is 1e-3 and updated by the Lambda LR [43] during the training process. All experiments are run on a single Ge Force RTX-4090 GPU, and the batch size of both the source and blended-target domains are set to 32. The hyper-parameters λe and λd, maximum iteration I, and mini-batch size B are also mentioned in Algorithm 1. |