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..
Fast Online Hashing with Multi-Label Projection
Authors: Wenzhe Jia, Yuan Cao, Junwei Liu, Jie Gui
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on two common benchmarks show that the proposed FOH can achieve dramatic superiority on query time up to 6.28 seconds less than state-of-the-art baselines with competitive retrieval accuracy. |
| Researcher Affiliation | Academia | 1 Ocean University of China, China 2 State Key Laboratory of Integrated Services Networks (Xidian University), China 3 Southeast University, China 4 Purple Mountain Laboratories, China |
| Pseudocode | No | The paper describes algorithms in prose and equations but does not contain any structured pseudocode or algorithm blocks with formal pseudocode syntax. |
| Open Source Code | Yes | Our implementation of this paper is publicly available on Git Hub at: https://github.com/caoyuan57/FOH. |
| Open Datasets | Yes | In this section, we conduct experiments on two common datasets: CIFAR-10 (Krizhevsky 2009) and FLICKR-25K (Huiskes and Lew 2008) to verify the efficiency and effectiveness of the proposed Fast Online Hashing (FOH). |
| Dataset Splits | No | The paper provides detailed information on training and test set creation and partitioning into blocks for stream data, but does not explicitly mention a separate 'validation' dataset split. |
| Hardware Specification | No | The paper mentions "the Big Data Computing Center of Southeast University for providing the facility support on the numerical calculations" but does not provide specific hardware details such as CPU/GPU models or memory specifications. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4). |
| Experiment Setup | Yes | Here, we provide the exact values of the parameter configurations in Tab. ??. Review that u denotes the number of the central points in the query pool, v denotes the number of the nearest neighbors of each central point, ฮฒ denotes the number of the returned central points when a new query arrives, {ฯ, ฮธ, ยต, ฮป, ฯ} denotes the hyper-parameters in the objective function. Table 2: Parameter configurations on two datasets. |