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 [1].
Robust Iterative Quantization for Efficient
Authors: โp
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. |