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..
Enhancing Domain Adaptation through Prompt Gradient Alignment
Authors: Viet Hoang Phan, Tung Lam Tran, Quyen Tran, Trung Le
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, our method consistently surpasses other vision language model adaptation methods by a large margin on a wide range of benchmarks. The implementation is available at https://github.com/Viet Hoang1512/PGA. 1 Introduction... 5 Experiments |
| Researcher Affiliation | Collaboration | Hoang Phan New York University EMAIL Lam Tran Vin AI Research EMAIL Quyen Tran Vin AI Research EMAIL Trung Le Monash University EMAIL |
| Pseudocode | Yes | Algorithm 1 Prompt gradient alignment for unsupervised domain adaptation |
| Open Source Code | Yes | The implementation is available at https://github.com/Viet Hoang1512/PGA. |
| Open Datasets | Yes | Datasets. We conduct experiments using three well-established UDA datasets of varying scales: Image CLEF [17], Office-Home [86], and Domain Net [87], respectively. Detailed descriptions of these datasets are available in Appendix C.1. |
| Dataset Splits | No | The paper states it follows protocols of prior work: 'following the same protocol of recent prompt-based UDA studies [25, 28]', but does not explicitly state the train/validation/test splits within the paper for the main experiments. |
| Hardware Specification | Yes | All experiments are run on Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz and NVIDIA A100-SXM4-80GB GPU. |
| Software Dependencies | No | The paper mentions using ResNet50, ResNet101, pretrained-CLIP, mini-batch SGD optimizer, and a cosine learning rate scheduler, but does not provide specific version numbers for any software or libraries. |
| Experiment Setup | Yes | For fair comparisons, we use a Res Net50 as our backbone on Image-CLEF and Office-Home and a Res Net101 on Domain Net. Their weights are taken from pretrained-CLIP and kept frozen during training. Prompts are trained with the mini-batch SGD optimizer with a learning rate of 0.003 and 0.005. We use a batch size of 32 and adopt a cosine learning rate scheduler. For hyper-parameters, token lengths M1 and M2 are both set to 16. Pseudo-label threshold τ is set to 0.4 for producing reliable labels. ρgn, ρga and λ are found using grid-search. Details are provided in the public source code. |