Distributed Composite Quantization
Authors: Weixiang Hong, Jingjing Meng, Junsong Yuan
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on ANN search and image retrieval tasks validate that the proposed DCQ significantly improves Composite Quantization in both efficiency and scale, while still maintaining competitive accuracy. |
| Researcher Affiliation | Academia | Weixiang Hong, Jingjing Meng, Junsong Yuan School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore {wxhong,jingjing.meng,JSYUAN}@ntu.edu.sg |
| Pseudocode | No | The paper describes the optimization process mathematically and textually but does not include a clearly labeled "Pseudocode" or "Algorithm" block. |
| Open Source Code | No | The paper states: "We use the publicly available implementation of L-BFGS 1. 1http://www.chokkan.org/software/liblbfgs/". This refers to a third-party library used, not the authors' own implementation code for their proposed method. |
| Open Datasets | Yes | We perform the ANN search experiments on three datasets: MNIST (Le Cun et al. 1998), ...; Label Me22K (Russell et al. 2008), ...; and SIFT1M (Jegou, Douze, and Schmid 2011),... |
| Dataset Splits | No | The paper mentions training data and test data but does not explicitly describe training/test/validation splits with percentages, counts, or references to predefined splits for reproducibility beyond stating "100K learning vectors and 10K queries" for SIFT1M dataset, which implies a training/test split, but not specifically validation, and not for all datasets. |
| Hardware Specification | Yes | Our machine is equipped with 24 Intel Xeon CPUs E5-2630 (2.30GHz) and 96 GB memory. ... We gratefully acknowledge the support of NVAITC (NVIDIA AI Technology Centre) for their donation of a Tesla K80 and M60 GPU used for our research at the ROSE Lab. |
| Software Dependencies | No | The paper mentions using "LBFGS algorithm (Nocedal 1980; Liu and Nocedal 1989)" and "publicly available implementation of L-BFGS", but it does not specify any version numbers for this or other software components. |
| Experiment Setup | Yes | We empirically set the penalty parameter ρ = 100 and the number of ADMM iterations I = 5. |