Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Benchmark and Asymmetrical-Similarity Learning for Practical Image Copy Detection
Authors: Wenhao Wang, Yifan Sun, Yi Yang
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that ASL outperforms state-of-the-art methods by a clear margin, confirming that solving the symmetric-asymmetric conflict is critical for ICD. |
| Researcher Affiliation | Collaboration | Wenhao Wang1,2*, Yifan Sun2, Yi Yang3 1 Re LER, University of Technology Sydney 2 Baidu Research 3 Zhejiang University EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | Yes | The NDEC dataset and code are available at https://github.com/Wang Wenhao0716/ASL. |
| Open Datasets | Yes | Based on existing ICD datasets, this paper constructs a new dataset by additionally adding 100, 000 and 24, 252 hard negative pairs into the training and test set, respectively. The NDEC dataset and code are available at https://github.com/Wang Wenhao0716/ASL. |
| Dataset Splits | No | The paper mentions 'training' and 'test' sets with specific image counts but does not provide details about a distinct 'validation' dataset split or how it was derived. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions various deep learning techniques and losses (e.g., Cos Face, triplet loss) but does not provide specific software dependencies with version numbers (e.g., PyTorch 1.9, Python 3.8). |
| Experiment Setup | No | The paper mentions replacing 8192-dim features with 2048-dim features and using Cos Face as a loss function, but it does not provide specific hyperparameters such as learning rate, batch size, or optimizer settings for the experimental setup. |