Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
IDEA: An Invariant Perspective for Efficient Domain Adaptive Image Retrieval
Authors: Haixin Wang, Hao Wu, Jinan Sun, Shikun Zhang, Chong Chen, Xian-Sheng Hua, Xiao Luo
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 25,235 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,082 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments conducted on benchmark datasets confirm the superior performance of our proposed IDEA compared to a variety of competitive baselines. |
| Researcher Affiliation | Collaboration | 1Peking University, 2University of Science and Technology of China, 3Terminus Group, 4University of California, Los Angeles |
| Pseudocode | Yes | Algorithm 1 Training Algorithm of IDEA |
| Open Source Code | No | The paper does not include a statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | Experiments are conducted on different benchmark datasets: (1) Office-Home dataset [57]: ... (2) Office-31 dataset [42]: ... (3) Digits dataset: We focus on MNIST [26] and USPS [23]... |
| Dataset Splits | No | The paper specifies a 'training set' and 'test queries' but does not explicitly mention a separate 'validation' set or its split. |
| Hardware Specification | Yes | We perform experiments on an A100-40GB GPU. |
| Software Dependencies | No | The paper mentions 'mini-batch SGD with momentum' for optimization but does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The batch size is set to 36 and the learning rate is fixed as 0.001. |