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..
Complementary Binary Quantization for Joint Multiple Indexing
Authors: Qiang Fu, Xu Han, Xianglong Liu, Jingkuan Song, Cheng Deng
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments carried out on two popular large-scale tasks including Euclidean and semantic nearest neighbor search demonstrate that the proposed CBQ method enjoys the strong table complementarity and significantly outperforms the state-of-the-arts, with up to 57.76% performance gains relatively. |
| Researcher Affiliation | Academia | 1 State Key Lab of Software Development Environment, Beihang University, China 2 Center for Future Media and School of Computer Science and Engineering, University of Electronic Science and Technology of China, China 3 School of Electronic Engineering, Xidian University, China |
| Pseudocode | Yes | Algorithm 1 Complementary Binary Quantization (CBQ). |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is open-source or available. |
| Open Datasets | Yes | In the experiments, we randomly select 10,000 and 1,000 samples as the training and the testing set respectively. ... We employ the two widely-used datasets SIFT-1M and GIST-1M [Jegou et al., 2011] ... we choose two widely-used large-scale image datasets: CIFAR-10 and NUS-WIDE. |
| Dataset Splits | No | The paper mentions training and testing sets, but does not explicitly describe a validation set or its split. |
| Hardware Specification | No | The paper does not specify the exact hardware (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | To start the algorithm, we initialize the prototypes P and the assignment m i for each samples using the classical K-means algorithm on the training data set. The number of clusters (or prototypes) is set to M = L 2b at first. Based on the initialization, we also estimate the scaling variable λ using the full binary codes in L hypercubes of b dimension... We set µ to 100 on SIFT-1M and 0.2 on GIST-1M. ... We set µ to 10 on CIFAR-10 and 20 on NUS-WIDE. |