Pairwise-Label-Based Deep Incremental Hashing with Simultaneous Code Expansion
Authors: Dayan Wu, Qinghang Su, Bo Li, Weiping Wang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on three widely-used image retrieval benchmarks, demonstrating that our method can significantly reduce the time required to expand existing database codes, while maintaining state-of-the-art retrieval performance. |
| Researcher Affiliation | Academia | Dayan Wu1, Qinghang Su1,2, Bo Li1*, Weiping Wang1 1Institute of Information Engineering, Chinese Academy of Sciences 2School of Cyber Security, University of Chinese Academy of Sciences |
| Pseudocode | No | The paper describes optimization steps and formulas but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about the release of its source code or a link to a code repository. |
| Open Datasets | Yes | We conduct extensive experiments on three public benchmark image retrieval datasets: CIFAR-10 (Krizhevsky and Hinton 2009), NUS-WIDE (Chua et al. 2009) and Image Net (Lin et al. 2014). |
| Dataset Splits | Yes | Following (Lai et al. 2015), we randomly select 1,000 images (100 images per class) as the test query set, and 5,000 images (500 images per class) as the training set. |
| Hardware Specification | No | The paper mentions running experiments 'with GPU' but does not specify any particular GPU model, CPU, or other hardware specifications. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | We tune λ in the range of [0.1, 1000] by fixing {γ = 1, q = 2000} for CIFAR-10, NUS-WIDE, and Image Net. Similarly, we set {λ = 1, q = 2000} for CIFAR-10, NUSWIDE, and Image Net when tuning γ. When tuning q, we set {λ = 1, γ = 1} for CIFAR-10, NUS-WIDE, and Image Net. |