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..
Nearest Neighbors Using Compact Sparse Codes
Authors: Anoop Cherian
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments are conducted on two state-of-the-art computer vision datasets with 1M data points and show an order of magnitude improvement in retrieval accuracy without sacrificing memory and query time compared to the state-of-the-art methods. |
| Researcher Affiliation | Academia | Anoop Cherian EMAIL INRIA, LEAR Project-team, Grenoble, France |
| Pseudocode | Yes | Algorithm 1 Sp ANN Indexing and Retrieval; Algorithm 2 IDL Algorithm |
| Open Source Code | No | The paper mentions using a third-party toolbox ("SPAMS toolbox (Mairal et al., 2010)") but does not state that the code for their own proposed methodology is open-source or provide a link. |
| Open Datasets | Yes | Our experiments are mainly based on the evaluation protocol of (Jegou et al., 2011) using two publicly available ANN datasets: (i) 1M SIFT and (ii) 1M GIST descriptors. |
| Dataset Splits | Yes | The first dataset is split into a training set with 100K 128-dimensional SIFT descriptors, a base set of 1M descriptors to be queried, and 10K query descriptors. Of the 100K training set, we use a random sample of 90K descriptors for learning the dictionary and 10K for validation. The GIST dataset consists of 960-dimensional descriptors and a training, database, and query step split of 500K, 1M, and 1K respectively. Of the training set, we use 400K descriptors for DL and 100K for validation. |
| Hardware Specification | Yes | Our timing comparisons are based on a single core 2.7 GHz AMD processor with 32GB memory. |
| Software Dependencies | No | The paper mentions using "SPAMS toolbox (Mairal et al., 2010)" and "MATLAB" but does not specify their version numbers or other software dependencies with version details. |
| Experiment Setup | Yes | For all the experiments, we used a fixed Jaccard threshold of ฮท = 0.33. We found ยต = 0.2 gave the best performance for dictionaries of sizes 256 and 512, while ยต = 0.3 performed best for 1024 atoms. We use M = 32 for our SIFT experiments and M = 64 for GIST. |