Semi-Supervised Deep Hashing with a Bipartite Graph
Authors: Xinyu Yan, Lijun Zhang, Wu-Jun Li
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on real datasets show that our BGDH outperforms state-of-the-art hashing methods. |
| Researcher Affiliation | Academia | Xinyu Yan, Lijun Zhang, Wu-Jun Li National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China {yanxy, zhanglj}@lamda.nju.edu.cn, liwujun@nju.edu.cn |
| Pseudocode | Yes | Algorithm 1 Context generation based on random walk |
| Open Source Code | No | The paper does not include an explicit statement about releasing the source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We conduct experiments on two widely used benchmark datasets: CIFAR-10 and NUS-WIDE. The CIFAR-10 dataset^1 consists of 60,000 images from 10 classes... ^1 https://www.cs.toronto.edu/ kriz/cifar.html |
| Dataset Splits | No | The paper describes query and training set sizes but does not explicitly define a separate validation set split (e.g., 'X% for validation' or 'Y samples for validation'). |
| Hardware Specification | Yes | All the experiments are performed on a NVIDIA K80 GPU server with Mat Conv Net [Vedaldi and Lenc, 2014]. |
| Software Dependencies | No | The paper mentions 'Mat Conv Net' as software used for experiments but does not specify a version number for it or any other software dependencies. |
| Experiment Setup | Yes | The bipartite graph of BGDH is constructed based on handcrafted features with heat kernel, where the hyper-parameter ρ is set as 1 for CIFAR-10 and 10 for NUS-WIDE. The hyper-parameter η in BGDH is set as 10 for CIFAR-10 and 100 for NUS-WIDE similar to DPSH [Li et al., 2016]. We simply set T1 = 10, T2 = 5, λ = 0.1 in all the experiments. |