Supervised Deep Hashing for Hierarchical Labeled Data
Authors: Dan Wang, Heyan Huang, Chi Lu, Bo-Si Feng, Guihua Wen, Liqiang Nie, Xian-Ling Mao
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two real-world public datasets show that the proposed method outperforms the state-of-the-art baselines in the image retrieval task. |
| Researcher Affiliation | Academia | 1Beijing Institute of Technology, China 2South China University of Technology, China 3Shandong University, China |
| Pseudocode | Yes | Algorithm 1 The Learning Algorithm for SHDH |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the proposed SHDH method is publicly available. |
| Open Datasets | Yes | We carried out experiments on two public benchmark datasets: CIFAR-100 and IAPRTC-12. CIFAR-100 is an image dataset containing 60,000 colour images of 32 32 pixels. It has 100 classes and each class contains 600 images. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image has a fine label (the class which it belongs to) and a coarse label (the superclass which it belongs to). Thus, the height of the hierarchical labels with a Root node in CIFAR-100 is three. The IAPRTC-12 dataset has 20,000 segmented images. |
| Dataset Splits | No | For both datasets, we randomly selected 90% as the training set and the left 10% as the test set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions pre-trained weights from VGG-F, but does not specify any software dependencies with version numbers (e.g., specific programming language versions, deep learning frameworks, or libraries). |
| Experiment Setup | Yes | The hyper-parameter α in SHDH is empirically set as one. The learning rate η is initialized as 0.01, and updated by η 2/3η empirically. The size of minibatch (default 128). |