Auxiliary Template-Enhanced Generative Compatibility Modeling
Authors: Jinhuan Liu, Xuemeng Song, Zhaochun Ren, Liqiang Nie, Zhaopeng Tu, Jun Ma
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | 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. |