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..
Robust Iterative Quantization for Efficient
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IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on benchmark datasets demonstrate that ITQ+ is overwhelmingly better than the original ITQ algorithm, especially when searching similarity in noisy data. ... 3 Experiment ... We carried out comprehensive experiments for similarity search. ... The results of ITQ+ and ITQ on two datasets with different binary code lengths are shown in Figure 2. |
| Researcher Affiliation | Academia | School of Software, Tsinghua University, Beijing 100084, China Northumbria University, Newcastle, NE1 8ST, UK |
| Pseudocode | Yes | Algorithm 1 Learning ITQ+ Input: Centered training data X; Parameters p 2 and q p; Output: Orthogonal matrix R; |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., URL or explicit statement) to the source code for the described methodology. |
| Open Datasets | Yes | The ๏ฌrst one is SIFT1M [J egou et al., 2011] which consists of 128-dimensional SIFT [Lowe, 2004] descriptors. ... The second dataset is GIST1M [J egou et al., 2011] which contains 960-dimensional GIST [Oliva and Torralba, 2001] descriptors. |
| Dataset Splits | No | The paper specifies training and query (test) set sizes but does not explicitly mention a distinct validation set or provide its split details. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU, GPU models, or memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | Following the settings in [Gong et al., 2013; He et al., 2013], we use Recall@R as the metric... In addition, following the setting in [Jegou et al., 2010; Gong et al., 2013], we ๏ฌrst centralize the data and perform a PCA to reduce the feature dimensionality to the length of binary codes. ... For ITQ+, we consistently set q = 1 when comparing to ITQ. ... For a fair comparison, we terminate the algorithm after 50 iterations in all experiments as suggested by ITQ. |