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].
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. |