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..
Towards Dynamic-Prompting Collaboration for Source-Free Domain Adaptation
Authors: Mengmeng Zhan, Zongqian Wu, Rongyao Hu, Ping Hu, Heng Tao Shen, Xiaofeng Zhu
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on three benchmark datasets showcase the superiority of our framework over previous SOTA methods. |
| Researcher Affiliation | Academia | School of Computer Science and Engineering, University of Electronic Science and Technology of China |
| Pseudocode | No | The paper describes the methodology in text and provides figures, but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | Yes | We evaluate our proposed approach on three standard benchmarks in domain adaptation including Office-31 [Saenko et al., 2010], Office-Home [Venkateswara et al., 2017], and Domain Net [Peng et al., 2019]. |
| Dataset Splits | No | The paper mentions using training and target datasets but does not provide specific percentages or counts for training, validation, or test splits, nor does it refer to predefined validation splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Vi T-B/16' and 'CLIP based on Vi T-B/16' as models, and 'Stochastic Gradient Descent (SGD) optimizer', but it does not specify version numbers for any underlying software dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | We optimize training objectives via the Stochastic Gradient Descent (SGD) [Zinkevich et al., 2010] optimizer, given a mini-batch size of 16, the momentum of 0.9, and weight decay ratio of 1 × 10−4, respectively. |