Locality Constrained Deep Supervised Hashing for Image Retrieval
Authors: Hao Zhu, Shenghua Gao
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on the CIFAR-10 and NUS-WIDE datasets show that our method significantly boosts the accuracy of image retrieval... 4 Experiments We compare our model with several baselines on two widely used benchmark datasets: CIFAR-10 [Krizhevsky and Hinton, 2009] and NUS-WIDE [Chua et al., 2009]. |
| Researcher Affiliation | Collaboration | Hao Zhu1, Shenghua Gao2 3M Cogent Beijing1 School of Information Science and Technology, Shanghai Tech University2 allenhaozhu@gmail.com, gaoshh@shanghaitech.edu.cn, |
| Pseudocode | No | The paper describes methods and equations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions comparing with other methods 'by using the codes provided by authors' for those methods, but it does not state that its own code is open-source or provide a link. |
| Open Datasets | Yes | We compare our model with several baselines on two widely used benchmark datasets: CIFAR-10 [Krizhevsky and Hinton, 2009] and NUS-WIDE [Chua et al., 2009]. |
| Dataset Splits | No | The paper specifies training and query (test) sets for CIFAR-10 and NUS-WIDE, but does not explicitly provide details for a separate validation split. |
| Hardware Specification | Yes | the model of GPU used in our experiments is GTX980-Ti which only has 6GB memory. ... It is worth noting that our CNN-F model can process more than 1200 images per second for feature extraction by using a GTX980-Ti GPU. |
| Software Dependencies | No | The paper mentions 'Our method is implemented with Mat Conv Net [Vedaldi and Lenc, 2015]' but does not provide a specific version number for the software. |
| Experiment Setup | No | The paper describes model architecture details and mentions convergence iterations but does not explicitly state concrete hyperparameter values such as learning rate, batch size, or optimizer settings. |