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..
Redundancy-resistant Generative Hashing for Image Retrieval
Authors: Changying Du, Xingyu Xie, Changde Du, Hao Wang
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show that our new method can significantly boost the quality of learned codes and achieve state-of-the-art performance for image retrieval. |
| Researcher Affiliation | Collaboration | Changying Du1, Xingyu Xie2, Changde Du3, Hao Wang1 1 360 Search Lab, Beijing 100015, China 2 College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing, China 3 Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository links or explicit statements of code release) for its methodology. |
| Open Datasets | Yes | We evaluate the proposed method on two computer vision tasks: 1) Image generation/reconstruction on MNIST [Oliva and Torralba, 2001]; 2) Image retrieval on CIFAR10 [Krizhevsky, 2009] and Caltech-256 [Griffin et al., 2007]. |
| Dataset Splits | No | The paper describes training and query/gallery splits for CIFAR-10 and Caltech-256 datasets but does not explicitly mention or specify a validation set split. |
| Hardware Specification | No | The paper mentions 'modern CPU/GPU' generally but does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Parameter Settings For the compared methods, we use the implementations provided by their authors (Deep-SGH is implemented directly based on SGH) and set the parameters according to their original papers. Without explicit statement, 1) for our R-SGH, the prior parameter ρj is set to 0.5 for any j {1, .., K}, the threshold parameter ϵ is set to 0.05, and both δ and η are set to 0.01; and 2) for R-SGH and Deep SGH, the encoder and decoder network structures are set as [D-K-K-K] and [K-K-K-D] respectively, where D and K are the dimensions of input data and hash code respectively. |