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..
Sub-Selective Quantization for Large-Scale Image Search
Authors: Yeqing Li, Chen Chen, Wei Liu, Junzhou Huang
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are carried out on three image benchmarks with up to one million samples, corroborating the efficacy of the sub-selective quantization method in terms of image retrieval. |
| Researcher Affiliation | Collaboration | 1 University of Texas at Arlington, Arlington, TX, 76019, USA 2IBM T. J. Watson Research Center, 10598, NY, USA |
| Pseudocode | Yes | Algorithm 1 PCA Quantization (PCAQ) ... Algorithm 2 Iterative Quantization (ITQ) ... Algorithm 3 ITQ with Sub-Selection (ITQ-SS) |
| Open Source Code | No | The paper does not provide concrete access information (e.g., specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | We evaluate the Sub-selective Quantization approaches on three public datasets: CIFAR (Krizhevsky and Hinton 2009) 1, MNIST2 and Tiny-1M (Wang, Kumar, and Chang 2012). 1http://www.cs.toronto.edu/ kriz/cifar.html 2http://yann.lecun.com/exdb/mnist/ |
| Dataset Splits | No | The paper specifies training and test splits for the datasets but does not explicitly mention or provide details for a validation split. |
| Hardware Specification | Yes | All our experiments were conducted on a desktop computer with a 3.4GHz Intel Core i7 and 12GB RAM. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions the sub-selection ratio and method initialization but does not provide other concrete experimental setup details, such as the specific number of iterations (N or p) used for ITQ/ITQ-SS in the experiments, which is a key hyperparameter. |