Unified Optimal Transport Framework for Universal Domain Adaptation
Authors: Wanxing Chang, Ye Shi, Hoang Tuan, Jingya Wang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments |
| Researcher Affiliation | Academia | Wanxing Chang1 Ye Shi1,3 Hoang Duong Tuan2 Jingya Wang1,3 1Shanghai Tech University 2University of Technology Sydney 3Shanghai Engineering Research Center of Intelligent Vision and Imaging |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at: https://github.com/changwxx/Uni OT-for-Uni DA. |
| Open Datasets | Yes | Office [31] contains 31 categories and about 4K images in 3 domains: Amazon(A), DSLR(D) and Webcam(W). Office-Home [37] contains 65 categories and about 15K images in 4 domains: Artistic images(Ar), Clip-Art images(Cl), Product images(Pr) and Real-World images(Rw). Vis DA [30] is a large dataset which conains 12 categories in source domain with 15K synthetic images and target domain with 5K real-world images. Domain Net [29] is the largest domain adaptation dataset which contains 345 categories and about 0.6 million in 6 domains but we only use 3 domain: Painting (P), Real (R), and Sketch (S) like [17]. |
| Dataset Splits | Yes | We follow the dataset split in [17] to conduct experiments. |
| Hardware Specification | Yes | All experiments are implemented on a GPU of NVIDIA TITAN V with 12GB. |
| Software Dependencies | No | The paper mentions 'POT [15]' but does not provide specific version numbers for software components like POT, PyTorch, or CUDA. |
| Experiment Setup | Yes | The batch size is set to 36 for both source and target domains. For all experiments, the initial learning rate is set to 1 10 2 for all new layers and 1 10 3 for pretrained backbone. The total training steps are set to be 10K for all datasets. For the pre-defined number of target prototypes, a larger size of target domain indicates a larger K. Therefore, we empirically set K = 50 for Office, K = 150 for Office-Home, K = 500 for Vis DA, K = 1000 for Domain Net. We default γ = 0.7, µ = 0.7, τ = 0.1, ε = 0.01, κ = 0.5 and λ = 0.1 for all datasets. We set the size of memory queue 2K for Office and Office-Home, 10K for Vis DA and Domain Net. |