Coupled CycleGAN: Unsupervised Hashing Network for Cross-Modal Retrieval
Authors: Chao Li, Cheng Deng, Lei Wang, De Xie, Xianglong Liu176-183
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | 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). |