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..
Dual Adversarial Graph Neural Networks for Multi-label Cross-modal Retrieval
Authors: Shengsheng Qian, Dizhan Xue, Huaiwen Zhang, Quan Fang, Changsheng Xu2440-2448
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments conducted on two cross-modal retrieval benchmark datasets, NUS-WIDE and MIRFlickr, indicate the superiority of DAGNN. |
| Researcher Affiliation | Academia | 1National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2University of Chinese Academy of Sciences 3Peng Cheng Laboratory |
| Pseudocode | No | The paper does not contain a clearly labeled pseudocode block or algorithm. |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | NUS-WIDE: we randomly pick up 2,000 image-text pairs as the testing set and the rest as the training set. MIRFlickr: 2,000 image-text pairs are randomly selected as the testing set and the rest are used for training. |
| Dataset Splits | No | The paper specifies training and testing sets, but does not explicitly mention a separate validation set or its size/proportion for hyperparameter tuning. It states "we validate the hyper-parameters α and β" but does not link this to a specific validation split. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU model, CPU type) used for running the experiments. |
| Software Dependencies | No | The paper mentions "implemented on Pytorch deep learning framework" but does not provide a specific version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | The batch size m is set as 1024 for NUS-WIDE and 100 for MIRFlickr. The initial learning rates of the optimizer are 0.00005 on both datasets. ... we validate the hyper-parameters α and β and finally set α = 0.2, β = 0.2 for both datasets. ... The multi-hop graph neural networks consist of five GAT layers on NUS-WIDE and four GAT layers on MIRFlickr together with one aggregation layer, in which the output dimensionality of each GAT layer and aggregation layer is 1,024. |