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 [1].
BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation
Authors: Mingcong Liu, Qiang Li, Zekui Qin, Guoxin Zhang, Pengfei Wan, Wen Zheng
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that Blend GAN outperforms state-of-the-art methods in terms of visual quality and style diversity for both latent-guided and reference-guided stylized face synthesis. Our project webpage is https://onionliu.github.io/Blend GAN/. We compare our model with several leading baselines on diverse image synthesis including Ada IN [25], MUNIT [16], FUNIT [17], DRIT++ [18], and Star GANv2 [19]. To evaluate the quality of our results, we use Frechet inception distance (FID) metric [50] to measure the discrepancy between the generated images and AAHQ dataset. |
| Researcher Affiliation | Industry | Mingcong Liu Y-tech, Kuaishou Technology EMAIL Qiang Li Y-tech, Kuaishou Technology EMAIL Zekui Qin Y-tech, Kuaishou Technology EMAIL Guoxin Zhang Y-tech, Kuaishou Technology EMAIL Pengfei Wan Y-tech, Kuaishou Technology EMAIL Wen Zheng Y-tech, Kuaishou Technology EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks are present in the paper. |
| Open Source Code | Yes | Our project webpage is https://onionliu.github.io/Blend GAN/ |
| Open Datasets | Yes | We use FFHQ [8] as the natural-face dataset, which includes 70,000 high-quality face images3. In addition, we build a new dataset of artistic-face images, Artstation-Artistic-face-HQ (AAHQ), consisting of 33,245 high-quality artistic faces at 10242 resolution (Figure 4). |
| Dataset Splits | No | The paper mentions using FFHQ and AAHQ datasets for training and evaluation but does not specify explicit training, validation, or test dataset splits or percentages. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments are provided in the paper. |
| Software Dependencies | No | The paper mentions that the code is based on a PyTorch implementation of StyleGAN2, but no specific version numbers for PyTorch or other software dependencies are provided. |
| Experiment Setup | No | The paper describes the model architecture and training objectives but does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, epochs). |