Unsupervised Cross-Domain Image Retrieval via Prototypical Optimal Transport
Authors: Bin Li, Ye Shi, Qian Yu, Jingya Wang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Proto OT surpasses existing state-of-the-art methods by a notable margin across benchmark datasets. Notably, on Domain Net, Proto OT achieves an average P@200 enhancement of 24.44%, and on Office-Home, it demonstrates a P@15 improvement of 12.12%.Experiments Datasets We evaluate our proposed method on two datasets: Office Home and Domain Net. |
| Researcher Affiliation | Academia | 1Shanghai Tech University 2Beihang University |
| Pseudocode | No | The paper describes the method in prose and mathematical equations but does not include a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | Code is available at https://github.com/HCVLAB/Proto OT. |
| Open Datasets | Yes | We evaluate our proposed method on two datasets: Office Home and Domain Net. The Office-Home (Venkateswara et al. 2017) dataset comprises 4 domains (Art, Clipart, Product, Real) encompassing 65 categories. ... The Domain Net (Peng et al. 2019) dataset consists of 6 domains (Clipart, Infograph, Painting, Quickdraw, Real, and Sketch). |
| Dataset Splits | No | The paper states 'We employ all available images' for Office-Home and uses 7 categories for Domain Net, but does not provide specific percentages or counts for training, validation, or test splits. Evaluation metrics are mentioned, but not the dataset split details for reproduction. |
| Hardware Specification | No | The paper states 'We employ the Res Net-50(He et al. 2016) architecture as the encoder fθ' but does not provide any specific details about the hardware (GPU, CPU, memory, etc.) used for experiments. |
| Software Dependencies | No | Implementation of our framework is in Py Torch(Paszke et al. 2019). The specific version number for PyTorch or any other software dependency is not explicitly provided. |
| Experiment Setup | Yes | Our optimization employs the Adam optimizer with a learning rate of 2.5 10 4 over 200 epochs, with a batch size of 64. ... For the Sinkhorn Algorithm(Cuturi 2013), the entropic regularization coefficient ϵ is set to 0.05 and following (Caron et al. 2020) the iterations is 3. The number of prototypes corresponds to the number of classes in the training set: 65 for Office-Home and 7 for Domain Net. |