Deep Hashing: A Joint Approach for Image Signature Learning
Authors: Yadong Mu, Zhu Liu
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive quantitative evaluations are conducted. On all adopted benchmarks, our proposed algorithm generates new performance records by significant improvement margins. |
| Researcher Affiliation | Collaboration | Yadong Mu,1 Zhu Liu2 1Institute of Computer Science and Technology, Peking University, China 2Multimedia Department, AT&T Labs, U.S.A. Email: myd@pku.edu.cn, zliu@research.att.com |
| Pseudocode | Yes | Algorithm 1 Deep Hash Algorithm |
| Open Source Code | No | The paper mentions implementing a customized version of the open-source Caffe but does not explicitly state that the custom code for their proposed method is open-source or provide access details. |
| Open Datasets | Yes | Description of Datasets: We conduct quantitative comparisons over four image benchmarks which represent different visual classification tasks. They include MNIST (Lecun et al. 1998) for handwritten digits recognition, CIFAR10 (Krizhevsky 2009) which is a subset of 80 million Tiny Images dataset and consists of images from ten animal or object categories, Kaggle-Face, which is a Kagglehosted facial expression classification dataset to stimulate the research on facial feature representation learning, and SUN397 (Xiao et al. 2010) which is a large scale scene image dataset of 397 categories. |
| Dataset Splits | No | The paper provides Train/Query Set sizes in Table 1 but does not explicitly describe a separate validation split or its size. |
| Hardware Specification | Yes | All the evaluations are conducted on a large-scale private cluster, equipped with 12 NVIDIA Tesla K20 GPUs and 8 K40 GPUs. |
| Software Dependencies | No | The paper mentions using "open-source Caffe (Jia 2013)" but does not specify its version number or any other software dependencies with their respective versions. |
| Experiment Setup | Yes | In all cases, the learning rate in gradient descent drops at a con-stant factor (0.1 in all of our experiments) until the training converges. |