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..
Feature Learning Based Deep Supervised Hashing with Pairwise Labels
Authors: Wu-Jun Li, Sheng Wang, Wang-Cheng Kang
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real datasets show that our DPSH method can outperform other methods to achieve the state-of-the-art performance in image retrieval applications. |
| Researcher Affiliation | Academia | Wu-Jun Li, Sheng Wang and Wang-Cheng Kang National Key Laboratory for Novel Software Technology Department of Computer Science and Technology, Nanjing University, China |
| Pseudocode | Yes | Algorithm 1 Learning algorithm for DPSH. |
| Open Source Code | No | The paper does not include any explicit statement or link indicating that the source code for the methodology described is publicly available. |
| Open Datasets | Yes | We compare our model with several baselines on two widely used benchmark datasets: CIFAR-10 and NUS-WIDE. |
| Dataset Splits | Yes | The hyper-parameter in DPSH is chosen by a validation set, which is 10 for CIFAR-10 and 100 for NUS-WIDE unless otherwise stated. |
| Hardware Specification | Yes | All our experiments for DPSH are completed with Mat Conv Net [Vedaldi and Lenc, 2015] on a NVIDIA K80 GPU server. |
| Software Dependencies | No | The paper mentions 'Mat Conv Net' as the framework used but does not provide a specific version number for it or any other software dependencies. |
| Experiment Setup | Yes | The hyper-parameter in DPSH is chosen by a validation set, which is 10 for CIFAR-10 and 100 for NUS-WIDE unless otherwise stated. |