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..
Heuristic Domain Adaptation
Authors: Shuhao Cui, Xuan Jin, Shuhui Wang, Yuan He, Qingming Huang
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on three challenging domain adaptation tasks, i.e., unsupervised domain adaptation, multi-source domain adaptation and semi-supervised domain adaptation. |
| Researcher Affiliation | Collaboration | 1Key Lab of Intell. Info. Process., Inst. of Comput. Tech., CAS 2University of Chinese Academy of Sciences 3Alibaba Group 4Peng Cheng Laboratory EMAIL, EMAIL EMAIL |
| Pseudocode | No | The paper describes algorithms and formulations but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | The code is available at https://github.com/cuishuhao/HDA. |
| Open Datasets | Yes | In UDA, we use a standard dataset Office-Home [51] with 15,500 images in 65 categories. For evaluation on MSDA, we utilize a challenging dataset Domain Net [39], which contains about 600,000 images in 345 categories. In SSDA, we utilize the standard dataset proposed by MME [43], which is selected from Domain Net [39]. |
| Dataset Splits | Yes | We also show the results under different settings of M and ablation study in Table 2. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments, only mentioning general training settings. |
| Software Dependencies | No | The paper mentions 'Py Torch' as an implementation framework but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | We employ Stochastic Gradient Descent (SGD) with a momentum of 0.9 and a weight decay of 0.0005 to train our model. We choose Res Net101 [24] as the basic backbone, and set the initial learning rate as 0.0003. We use the Res Net34 [24] as the backbones of the generator and fix the number of sub-networks to be 3, and with the initial learning rate as 0.001. |