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..
Optimal Projection Guided Transfer Hashing for Image Retrieval
Authors: Ji Liu, Lei Zhang8754-8761
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various benchmark databases verify that our method outperforms many state-of-the-art learning to hash methods. The implementation details are available at https://github.com/liuji93/GTH. |
| Researcher Affiliation | Academia | Ji Liu, Lei Zhang School of Microelectronics and Communication Engineering, Chongqing University, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Optimal Projection Guided Transfer Hashing |
| Open Source Code | Yes | The implementation details are available at https://github.com/liuji93/GTH. |
| Open Datasets | Yes | We perform the experiments on three groups benchmark datasets: PIE-C29&PIE-C05 from PIE (Sim, Baker, and Bsat 2002), Amazon&Dslr from Office (Saenko et al. 2010), and VOC2007&Caltech101 from VLCS (Torralba and Efros 2011). |
| Dataset Splits | No | The paper specifies training and testing sets, but does not explicitly mention a separate validation set or its size/percentage for model selection. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | For our GTH, we empirically set λ1 to 0.1 and λ2 to 1. The code length is set to 32. ... where τ denotes the step size. We empirically set τ = 0.1. |