Deep Hashing Network for Efficient Similarity Retrieval
Authors: Han Zhu, Mingsheng Long, Jianmin Wang, Yue Cao
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on standard image retrieval datasets show the proposed DHN model yields substantial boosts over latest state-of-the-art hashing methods. We conduct extensive experiments to evaluate the efficacy of the proposed DHN model against several state-of-the-art hashing methods on three widely-used benchmark datasets. |
| Researcher Affiliation | Academia | Han Zhu, Mingsheng Long, Jianmin Wang and Yue Cao School of Software, Tsinghua University, Beijing, China Tsinghua National Laboratory for Information Science and Technology {zhuhan10,caoyue10}@gmail.com {mingsheng,jimwang}@tsinghua.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The codes and configurations will be made available online. |
| Open Datasets | Yes | NUS-WIDE1 is a public web image dataset. CIFAR-102 is a dataset containing 60,000 color images in 10 classes. Flickr3 consists of 25,000 images collected from Flickr. |
| Dataset Splits | Yes | We cross-validate the learning rate from 10 5 to 10 2 with a multiplicative step-size 10. We choose the quantization penalty parameter λ by cross-validation from 10 5 to 102 with a multiplicative step-size 10. |
| Hardware Specification | No | The paper mentions using the Caffe framework and Alex Net architecture but does not specify any hardware details (e.g., CPU, GPU models, or cloud computing resources) used for the experiments. |
| Software Dependencies | No | The paper mentions using the "Caffe framework (Jia et al. 2014)" but does not provide specific version numbers for Caffe or any other software dependencies. |
| Experiment Setup | Yes | As the fch layer is trained from scratch, we set its learning rate to be 10 times that of the lower layers. We use the mini-batch stochastic gradient descent (SGD) with 0.9 momentum and the learning rate annealing strategy implemented in Caffe... We fix the mini-batch size of images as 64 and the weight decay parameter as 0.0005. |