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..
Equally-Guided Discriminative Hashing for Cross-modal Retrieval
Authors: Yufeng Shi, Xinge You, Feng Zheng, Shuo Wang, Qinmu Peng
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 3 Experiments 3.1 Datasets Performance evaluation was conducted on two benchmark datasets: MIRFLICKR-25K [Huiskes and Lew, 2008] and MS COCO [Lin et al., 2014]. |
| Researcher Affiliation | Academia | 1School of Electronic Information and Communications, Huazhong University of Science and Technology 2Department of Computer Science and Engineering, Southern University of Science and Technology |
| Pseudocode | Yes | Algorithm 1 Equally-Guided Discriminative Hashing |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for their method is open-source or publicly available. |
| Open Datasets | Yes | Performance evaluation was conducted on two benchmark datasets: MIRFLICKR-25K [Huiskes and Lew, 2008] and MS COCO [Lin et al., 2014]. |
| Dataset Splits | No | For both datasets, 10000 image-text pairs are randomly chosen from retrieval set for training. The paper mentions the original MS COCO dataset has training and validation images, but does not specify a validation split created for *their* experiments. |
| Hardware Specification | Yes | We implement all deep learning methods with Tensorflow on a NVIDIA 1080ti GPU server. |
| Software Dependencies | No | We implement all deep learning methods with Tensorflow on a NVIDIA 1080ti GPU server. The paper mentions TensorFlow but does not specify a version number or other software dependencies with versions. |
| Experiment Setup | Yes | We set hyper-parameters as: α = β = γ = 1. To learn neural network parameters, we apply the Adam solver with a learning rate within 10 2 10 6 and set batch size as 128. |