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..
Deep Joint Semantic-Embedding Hashing
Authors: Ning Li, Chao Li, Cheng Deng, Xianglong Liu, Xinbo Gao
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three benchmark datasets show that the proposed model outperforms current state-of-the-art methods. ... 4 Experiments |
| Researcher Affiliation | Academia | 1 School of Electronic Engineering, Xidian University, Xi an 710071, China 2 Beihang University, Beijing 100191, China |
| Pseudocode | Yes | Algorithm 1 The learning algorithm for our DSEH |
| Open Source Code | No | The paper states 'Our model is implemented on Tensor Flow [Abadi et al., 2016]', but it does not provide any explicit statement or link for the open-source code of their DSEH method. |
| Open Datasets | Yes | The experiments are conducted on three benchmark image retrieval datasets: NUS-WIDE [Chua et al., 2009], Image Net [Russakovsky et al., 2015], and MS-COCO [Lin et al., 2014]. |
| Dataset Splits | No | The paper states 'The learning rate is chosen from 10^-2 to 10^-6 with a validation set', implying a validation set was used. However, it does not explicitly specify the size or percentage of the validation split for any of the datasets. |
| Hardware Specification | Yes | Our model is implemented on Tensor Flow [Abadi et al., 2016] on a server with two NVIDIA TITAN X GPUs. |
| Software Dependencies | No | The paper states 'Our model is implemented on Tensor Flow [Abadi et al., 2016]', but it does not specify a version number for TensorFlow or any other software dependencies. |
| Experiment Setup | Yes | The learning rate is chosen from 10^-2 to 10^-6 with a validation set. The batch size of Lab Net and Img Net are set to 32 and 128 respectively. For the hyper-parameters in Lab Net, we conduct cross-validation to search α and γ from 10^-3 to 10^2, and search β from 10^-6 to 10^-1. We find that the optimal result can be obtained when α = γ = 1, and β = 0.005. Then we search from 10^-3 to 10^2 and discover η = 1 is the best for Img Net. |