Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation
Authors: Shiqi Yang, yaxing wang, kai wang, Shangling Jui, Joost van de Weijer
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results prove the superiority of our method, and our method can be adopted as a simple but strong baseline for future research in SFDA. Our method can be also adapted to source-free open-set and partial-set DA which further shows the generalization ability of our method. Code is available in https://github.com/Albert0147/Aa D_SFDA. |
| Researcher Affiliation | Collaboration | Shiqi Yang1, Yaxing Wang2 , Kai Wang1, Shangling Jui3, Joost van de Weijer1 1 Computer Vision Center, Universitat Autonoma de Barcelona, Barcelona, Spain 2 Nankai University, Tianjin, China 3 Huawei Kirin Solution, Shanghai, China |
| Pseudocode | Yes | Algorithm 1 Attracting and Dispersing for SFDA Require: Source-pretrained model and target data Dt 1: Build memory bank storing all target features and predictions 2: while Adaptation do 3: Sample batch T from Dt and Update memory bank 4: For each feature zi in T , retrieve K-nearest neighbors (Ci) and their predictions from memory bank 5: Update model by minimizing Eq. 5 6: end while |
| Open Source Code | Yes | Code is available in https://github.com/Albert0147/Aa D_SFDA. |
| Open Datasets | Yes | We conduct experiments on three benchmark datasets for image classification: Office-31, Office-Home and Vis DA-C 2017. |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits, such as specific percentages or counts for validation data. |
| Hardware Specification | No | The paper states: 'Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [No]' |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used (e.g., PyTorch version, CUDA version). |
| Experiment Setup | Yes | We adopt SGD with momentum 0.9 and batch size of 64 for all datasets. The learning rate for Office-31 and Office-Home is set to 1e-3 for all layers, except for the last two newly added fc layers, where we apply 1e-2. Learning rates are set 10 times smaller for Vis DA. We train 40 epochs for Office-31 and Office-Home while 15 epochs for Vis DA. |