Correspondence-Free Domain Alignment for Unsupervised Cross-Domain Image Retrieval
Authors: Xu Wang, Dezhong Peng, Ming Yan, Peng Hu
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To verify the effectiveness of the proposed method, extensive experiments are conducted on four benchmark datasets compared with six state-of-the-art methods. |
| Researcher Affiliation | Collaboration | Xu Wang1, Dezhong Peng1,3,4, Ming Yan2, Peng Hu1* 1College of Computer Science, Sichuan University, Chengdu, China 2Centre for Frontier AI Research (CFAR), A*STAR, Singapore 3Sichuan Zhiqian Technology Co., Ltd, Chengdu, China 4Chengdu Ruibei Yingte Information Technology Ltd. Company, Chengdu, China |
| Pseudocode | Yes | Algorithm 1 briefly summarizes the optimization procedure of the proposed Co DA approach. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | To verify the effectiveness of the proposed method, we conduct extensive experiments on four benchmark datasets, i.e., Office31 (Saenko et al. 2010), Image-CLEF (Long et al. 2017), Office Home (Venkateswara et al. 2017), and Adaptiope (Ringwald and Stiefelhagen 2021). |
| Dataset Splits | No | For each dataset, we randomly partition the data into training and test sets, with an 80-20 ratio for each category. No explicit mention of a separate validation set. |
| Hardware Specification | Yes | The proposed approach is implemented by Py Torch with two Nvidia Ge Force RTX 2080 GPUs. |
| Software Dependencies | No | The paper mentions "implemented by Py Torch" but does not specify a version number for PyTorch or other software dependencies. |
| Experiment Setup | Yes | For a fair comparison, the hyper-parameters are set as η = 0.95 nb = 16, λ = 0.01, Ne = 20, τ = 0.01, and α = 0.003 for all datasets. |