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..
Universal Domain Adaptation through Self Supervision
Authors: Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Kate Saenko
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show through extensive experiments that DANCE outperforms baselines across open-set, open-partial, and partial domain adaptation settings. Implementation is available at https://github.com/Vision Learning Group/DANCE. |
| Researcher Affiliation | Collaboration | Kuniaki Saito1 Donghyun Kim1 Stan Sclaroff1 Kate Saenko1,2 1Boston University 2MIT-IBM Watson AI Lab [keisaito,dohnk,sclaroff,saenko]@bu.edu |
| Pseudocode | No | No structured pseudocode or algorithm blocks found. |
| Open Source Code | Yes | Implementation is available at https://github.com/Vision Learning Group/DANCE. |
| Open Datasets | Yes | As the most prevalent benchmark dataset, we use Office [32], which has three domains (Amazon (A), DSLR (D), Webcam (W)) and 31 classes. The second benchmark dataset Office Home (OH) [40] contains four domains and 65 classes. The third dataset Vis DA (VD) [30] contains 12 classes from two domains: synthetic and real images. We provide an analysis of varying the number of classes using Caltech [14] and Image Net [8] because these datasets contain a large number of classes. |
| Dataset Splits | Yes | We follow the settings of CDAN [25] for closed (CDA), SAN [2] for partial (PDA), STA [23] for open-set (ODA), and UAN [43] for open-partial domain adaptation (OPDA) in our experiments. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) are mentioned for running experiments. The paper states 'All experiments are implemented in Pytorch [29]. We employ ResNet50 [17] pre-trained on Image Net [8] as the feature extractor in all experiments.' but no hardware specs. |
| Software Dependencies | No | All experiments are implemented in Pytorch [29]. We employ Res Net50 [17] pre-trained on Image Net [8] as the feature extractor in all experiments. No specific version numbers for Pytorch or other libraries are provided. |
| Experiment Setup | Yes | We set λ in Eq. 9 as 0.05 and m in Eq. 7 as 0.5 for our method. For all comparisons, we use the same hyper-parameters, batch-size, learning rate, and checkpoint. The analysis of sensitivity to hyper-parameters is discussed in the supplementary. |