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..
Coupled CycleGAN: Unsupervised Hashing Network for Cross-Modal Retrieval
Authors: Chao Li, Cheng Deng, Lei Wang, De Xie, Xianglong Liu176-183
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three popular benchmark datasets show that the proposed UCH outperforms the state-of-the-art unsupervised cross-modal hashing methods. |
| Researcher Affiliation | Academia | 1School of Electronic Engineering, Xidian University, Xi an 710071, China 2State Key Lab of Software Development Environment, Beihang University, Beijing 100191, China |
| Pseudocode | Yes | Algorithm 1 Optimizing process of the proposed UCH |
| Open Source Code | No | The paper does not provide any specific links to source code or explicitly state that the code for their methodology is made open-source or available. |
| Open Datasets | Yes | Three popular benchmark datasets in cross-modal retrieval: MIRFlickr-25K (Huiskes and Lew 2008), NUSWIDE (Chua et al. 2009), and Microsoft COCO (Lin et al. 2014) are adopted to validate our proposed method. |
| Dataset Splits | No | The paper describes 'training set' and 'retrieval set' for different datasets (e.g., 'For supervised baselines, 5,000 image-text pairs are selected from retrieval set to construct training set.'), but does not explicitly provide details about a distinct 'validation' dataset split used for hyperparameter tuning or early stopping. |
| Hardware Specification | Yes | UCH is implemented via Tensor Flow and executed on a server with two NVIDIA TITAN X GPUs. |
| Software Dependencies | No | The paper mentions 'Tensor Flow' as the implementation framework, but does not provide specific version numbers for TensorFlow or any other software libraries or dependencies. |
| Experiment Setup | Yes | In all our experiments, the initial leaning rates of image and text networks are set to 10 4 and 10 2. And batchsize and weight decay are set to 128 and 10 1. ... GI T f and GT I f are constructed with two different deep networks with four full-connected layers, e.g., (GI T f :4096 512 256 512 300 and GT I f : 300 512 256 512 4096). |