Deep Multimodal Hashing with Orthogonal Regularization
Authors: Daixin Wang, Peng Cui, Mingdong Ou, Wenwu Zhu
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on WIKI and NUS-WIDE, demonstrate a substantial gain of DMHOR compared with state-of-the-art methods. |
| Researcher Affiliation | Academia | 1Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology, Tsinghua University. Beijing, China |
| Pseudocode | Yes | Algorithm 1 Fine-tuning for DMHOR |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about the availability of open-source code for the described methodology. |
| Open Datasets | Yes | NUS-WIDE [Chua et al., 2009] is a public web image dataset [...] WIKI [Rasiwasia et al., 2010] is a web document dataset |
| Dataset Splits | No | The paper specifies training and test sets but does not explicitly mention a separate validation set with specific split information. |
| Hardware Specification | Yes | We run the following experiments with implementation in Matlab on a machine running Windows Server 2008 with 12 2.39GHz cores and 192 GB of memory. |
| Software Dependencies | No | The paper mentions "implementation in Matlab" but does not specify any version numbers for Matlab or any other software libraries or dependencies. |
| Experiment Setup | Yes | The hyper-parameters of λ, µ and ν are set as 0.5, 0.5 and 0.001 by using grid search. |