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..
Pairwise-Label-Based Deep Incremental Hashing with Simultaneous Code Expansion
Authors: Dayan Wu, Qinghang Su, Bo Li, Weiping Wang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on three widely-used image retrieval benchmarks, demonstrating that our method can significantly reduce the time required to expand existing database codes, while maintaining state-of-the-art retrieval performance. |
| Researcher Affiliation | Academia | Dayan Wu1, Qinghang Su1,2, Bo Li1*, Weiping Wang1 1Institute of Information Engineering, Chinese Academy of Sciences 2School of Cyber Security, University of Chinese Academy of Sciences |
| Pseudocode | No | The paper describes optimization steps and formulas but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about the release of its source code or a link to a code repository. |
| Open Datasets | Yes | We conduct extensive experiments on three public benchmark image retrieval datasets: CIFAR-10 (Krizhevsky and Hinton 2009), NUS-WIDE (Chua et al. 2009) and Image Net (Lin et al. 2014). |
| Dataset Splits | Yes | Following (Lai et al. 2015), we randomly select 1,000 images (100 images per class) as the test query set, and 5,000 images (500 images per class) as the training set. |
| Hardware Specification | No | The paper mentions running experiments 'with GPU' but does not specify any particular GPU model, CPU, or other hardware specifications. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | We tune λ in the range of [0.1, 1000] by fixing {γ = 1, q = 2000} for CIFAR-10, NUS-WIDE, and Image Net. Similarly, we set {λ = 1, q = 2000} for CIFAR-10, NUSWIDE, and Image Net when tuning γ. When tuning q, we set {λ = 1, γ = 1} for CIFAR-10, NUS-WIDE, and Image Net. |