Self-Supervised Sketch-to-Image Synthesis
Authors: Bingchen Liu, Yizhe Zhu, Kunpeng Song, Ahmed Elgammal2073-2081
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on 10242 resolution demonstrate a new state-of-art-art performance of the proposed model on Celeb A-HQ and Wiki-Art datasets. |
| Researcher Affiliation | Collaboration | 1 Playform Artrendex Inc., USA 2 Department of Computer Science, Rutgers University |
| Pseudocode | No | The paper describes methods through text, equations, and diagrams, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for their method. |
| Open Datasets | Yes | We evaluate our model on two datasets, Celeb AHQ (Liu et al. 2015; Lee et al. 2020a) and Wiki Art 1. 1https://www.wikiart.org/ |
| Dataset Splits | No | The paper specifies training and testing splits ('test on the rest images') but does not explicitly mention a separate validation split or its size/proportion. |
| Hardware Specification | Yes | The whole training process takes only 20 minutes on one RTX-2080 GPU. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | Ls tri = max(cos(f t s, f org s ) cos(f t s, f neg s ) + α, 0), where α is the margin. Lc tri = max(d(f t c, f pos c ) d(f t c, f neg c ) + β, 0), where d(, ) is the mean-squared distance, β is the margin. λ is the weight for the reconstruction term which we set to 10 for all datasets. |