Deep Joint Semantic-Embedding Hashing
Authors: Ning Li, Chao Li, Cheng Deng, Xianglong Liu, Xinbo Gao
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | 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. |