Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Hamming Compatible Quantization for Hashing
Authors: Zhe Wang, Ling-Yu Duan, Jie Lin, Xiaofang Wang, Tiejun Huang, Wen Gao
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiment results have shown our approach significantly improves the performance of various stateof-the-art hashing methods while maintaining fast retrieval speed. |
| Researcher Affiliation | Academia | Zhe Wang, Ling-Yu Duan, Jie Lin, Xiaofang Wang, Tiejun Huang, Wen Gao The Institute of Digital Media, Peking University, Beijing, China EMAIL |
| Pseudocode | Yes | Algorithm 1 shows the pseudo-code. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing its source code or a link to a code repository. |
| Open Datasets | Yes | Extensive experiments were carried out over three widely used retrieval benchmark datasets, Label Me22K [Torralba et al., 2008], CIFAR-10 [Krizhevsky, 2009] and NUSWIDE [Chua et al., 2009]. |
| Dataset Splits | No | The paper mentions using a random selection of 1000 images for queries and the remaining for the database, and selecting 1000 images for training the quantization boundaries. It discusses parameter tuning using λ, which implies a validation process, but does not explicitly provide percentages or counts for training/validation/test splits, nor does it refer to standard validation splits for the entire dataset used for training their model (only for the quantization boundaries). |
| Hardware Specification | Yes | We measure the search time on an Intel(R) Core(TM) i5 3470 CPU at 3.20GHz with a single thread. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers. |
| Experiment Setup | Yes | In the following experiments, we set λ = 0.6, 0.7, 0.8, 0.9 at code size 32, 64, 128, 256, respectively. |