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..
Online Hashing with Efficient Updating of Binary Codes
Authors: Zhenyu Weng, Yuesheng Zhu12354-12361
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments on two multi-label image databases demonstrate the effectiveness and the ef๏ฌciency of our method for multi-label image retrieval. |
| Researcher Affiliation | Academia | Zhenyu Weng, Yuesheng Zhu Communication and Information Security Laboratory, Shenzhen Graduate School, Peking University EMAIL |
| Pseudocode | Yes | Algorithm 1 OHWEU Input: streaming data (xi, yi), C, K Output: P and R 1: Learn {wk, bk}K k=1 by PCA-ITQ in the initial stage 2: Initialize P0 = [p0 1, ..., p0 K] and R0 = [r0 1, ..., r0 K] 3: for i = 1, 2, ... do 4: Obtain the binary code g i according to Eqn.(5) 5: for j = 1 to K do 6: Update pi j according to Eqn. (13) 7: Update ri j according to Eqn. (28) 8: end for 9: end for |
| Open Source Code | No | The paper does not provide any statement about making its source code publicly available or a link to a code repository. |
| Open Datasets | Yes | Datasets We compare our method on two commomly-used multilabel image datasets, MS-COCO and NUS-WIDE. (a).MS-COCO dataset (Lin et al. 2014). ... (b). NUS-WIDE dataset (Chua et al. ). |
| Dataset Splits | Yes | (a).MS-COCO dataset (Lin et al. 2014). ... We randomly take 4000 images as the queries, and the rest for training and searching. ... (b). NUS-WIDE dataset (Chua et al. ). ... We randomly take 2000 images as the queries, and the rest for training and searching. |
| Hardware Specification | Yes | The experiments are run on a computer with CPU I7, 24GB memory. ... The experiments are run on a PC with Intel i7 3.4 GHz CPU, 24 GB memory. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | As OKH does, we have m = 300 data points to train the hash functions in the initial stage. ... Based on the results, we set C = 0.1 for MS-COCO and NUS-WIDE. |