Prototypical Partial Optimal Transport for Universal Domain Adaptation
Authors: Yucheng Yang, Xiang Gu, Jian Sun
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on four benchmarks show that our method outperforms the previous state-of-the-art Uni DA methods. In experiments, we evaluate our method on four Uni DA benchmarks. Experimental results show that our method performs favorably compared with the state-of-the-art methods for Uni DA. |
| Researcher Affiliation | Academia | Yucheng Yang*, Xiang Gu*, Jian Sun School of Mathematics and Statistics, Xi an Jiaotong University, Xi an, China {ycyang, xianggu}@stu.xjtu.edu.cn, jiansun@xjtu.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/ycyangxjtu/PPOT. |
| Open Datasets | Yes | Datasets. Office-31 (Saenko et al. 2010) includes 4652 images in 31 categories from 3 domains: Amazon (A), DSLR (D), and Webcam (W). Office-Home (Venkateswara et al. 2017) consists of 15500 images in 65 categories... Vis DA (Peng et al. 2017) is a larger dataset... Domain Net (Peng et al. 2019) is one of the most challenging datasets... |
| Dataset Splits | No | The paper describes the datasets and how evaluation is performed (e.g., accuracy for all target samples, H-score), but it does not provide specific train/validation/test dataset split percentages or sample counts for any of the datasets used. |
| Hardware Specification | Yes | We implement our method using Pytorch (Paszke et al. 2019) on a single Nvidia RTX A6000 GPU. |
| Software Dependencies | No | The paper mentions 'Pytorch' and 'Moco V2' but does not provide specific version numbers for these software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | In training phase, we optimize the model using Nesterov momentum SGD with momentum of 0.9 and weight decay of 5 10 4. Following (Ganin and Lempitsky 2015), the learning rate decays with the factor of (1 + αt) β, where t linearly changes from 0 to 1 in training, and we set α = 10, β = 0.75. The batch size is set to 72 in all experiments except in Domain Net tasks where it is changed to 256. We train our model for 5 epochs (1000 iterations per epoch)... The initial learning rate is set to 1 10 4 on Office-31, 5 10 4 on Office-Home and Vis DA, and 0.01 on Domain Net. |