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..
SPA: A Graph Spectral Alignment Perspective for Domain Adaptation
Authors: Zhiqing Xiao, Haobo Wang, Ying Jin, Lei Feng, Gang Chen, Fei Huang, Junbo Zhao
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On standardized benchmarks, the extensive experiments of SPA demonstrate that its performance has surpassed the existing cutting-edge DA methods. Coupled with dense model analysis, we conclude that our approach indeed possesses superior efficacy, robustness, discriminability, and transferability. Code and data are available at: https://github.com/Crown X/SPA. We conduct extensive evaluations on several benchmark datasets including Domain Net, Office Home, Office31, and Vis DA2017. The exprimental results show that our method consistently outperforms existing state-of-the-art domain adaptation methods |
| Researcher Affiliation | Collaboration | Zhiqing Xiao13, Haobo Wang23, Ying Jin4, Lei Feng5, Gang Chen13, Fei Huang6, Junbo Zhao13 1 College of Computer Science and Technology, Zhejiang University 2 School of Software Technology, Zhejiang University 3 Key Lab of Intelligent Computing based Big Data of Zhejiang Province, Zhejiang University 4 CUHK-Sense Time Joint Lab, The Chinese University of Hong Kong 5 School of Computer Science and Engineering, Nanyang Technological University 6 Alibaba Group |
| Pseudocode | No | No explicit pseudocode or algorithm block was provided. |
| Open Source Code | Yes | Code and data are available at: https://github.com/Crown X/SPA. |
| Open Datasets | Yes | We conduct experiments on 4 benchmark datasets: 1) Office31 [63] is a widely-used benchmark for visual DA. ... 2) Office Home [76] is a challenging dataset ... 3) Vis DA2017 [58] is a large-scale benckmark ... 4) Domain Net [57] is a large-scale dataset |
| Dataset Splits | Yes | Following the standard protocols for unsupervised domain adaptation in previous methods [48, 56], we use the same backbone networks for fair comparisons. ... The reverse validation [47, 89] is conducted to select hyper-parameters. For both unsupervised domain adaptation (UDA) and semi-supervised domain adaptation (SSDA) scenarios, we fix the coefficient of Lnap as 0.2 and the coefficient of Lgsa as 1.0, while we will offer a sensitivity analysis for this two coefficients in the following section. |
| Hardware Specification | Yes | All experiments are conducted on a server with the following configurations: Operating System: Ubuntu 20.04.4 LTS CPU: Intel(R) Xeon(R) Platinum 8358P CPU @ 2.60GHz, 32 cores, 128 processors GPU: NVIDIA Ge Force RTX 3090 |
| Software Dependencies | No | We use Py Torch and tllib toolbox [28] to implement our method and finetune Res Net pre-trained on Image Net [25, 26]. |
| Experiment Setup | Yes | We adopt mini-batch stochastic gradient descent (SGD) with a momentum of 0.9, a weight decay of 0.005, and an initial learning rate of 0.01, following the same learning rate schedule in [48]. ... The learning rates of the layers trained from scratch are set to be 0.01. We use the the same learning rate schedule in [48, 52], including a learning rate scheduler with a momentum of 0.9, a weight decay of 0.005, the bottleneck size of 256, and batch size of 32. |