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..
Auxiliary Template-Enhanced Generative Compatibility Modeling
Authors: Jinhuan Liu, Xuemeng Song, Zhaochun Ren, Liqiang Nie, Zhaopeng Tu, Jun Ma
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two real-world datasets demonstrate the superiority of the proposed approach. |
| Researcher Affiliation | Collaboration | 1Shandong University, Qingdao, China 2Tencent AI Lab, Shenzhen, China |
| Pseudocode | No | The paper includes architectural diagrams (Figure 2) but no pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | we conduct extensive experiments on public datasets: Fashion VC [Song et al., 2017] and Exp Fashion [Lin et al., 2019] |
| Dataset Splits | Yes | For each dataset, we randomly select 80% for training, 10% for validation, and the rest for testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., CPU/GPU models, memory). |
| Software Dependencies | No | The paper mentions using 'Text CNN [Kim, 2014]' but does not specify version numbers for any software dependencies or libraries. |
| Experiment Setup | No | The paper describes the model architecture and losses but does not explicitly state specific hyperparameters (e.g., learning rate, batch size, epochs) or detailed training configurations. |