https://caiyf03.github.io/tags/stylegan2/StyleGAN2HugoBlox Kit (https://hugoblox.com)en-usSun, 01 Mar 2026 00:00:00 +0000https://caiyf03.github.io/media/icon_hu_702a800cd775dbac.pnghttps://caiyf03.github.io/tags/stylegan2/https://caiyf03.github.io/internships/alibaba/Sun, 01 Mar 2026 00:00:00 +0000https://caiyf03.github.io/internships/alibaba/<h2 id="overview">Overview</h2> <p><strong>Algorithm Intern</strong> <em>Mar. 2026 – Apr. 2026</em></p> <h3 id="portrait-to-anime-generation-system">Portrait-to-Anime Generation System</h3> <ul> <li>Developed portrait stylization models based on StyleGAN2 and diffusion architectures for high-quality anime-style image generation.</li> <li>Constructed end-to-end training workflows including dataset preprocessing, data augmentation, model training, inference, and quantitative evaluation.</li> <li>Built scalable pipelines supporting rapid experimentation across multiple generative model architectures.</li> </ul> <h3 id="model-optimization-and-training-stability">Model Optimization and Training Stability</h3> <ul> <li>Improved training stability and convergence performance using PyTorch and TensorFlow through optimizer tuning, learning-rate scheduling, and architecture refinement.</li> <li>Conducted systematic hyperparameter exploration to improve image fidelity, visual quality, and generation consistency.</li> <li>Evaluated generation performance using both objective metrics and human perceptual assessments.</li> </ul> <h3 id="representation-enhancement">Representation Enhancement</h3> <ul> <li>Explored attention-based feature enhancement and LoRA-based parameter-efficient adaptation techniques for controllable style transfer.</li> <li>Improved preservation of facial identity features while maintaining target anime-style characteristics.</li> <li>Enhanced style consistency and fine-grained visual details across generated outputs.</li> </ul> <h3 id="experimental-platform-development">Experimental Platform Development</h3> <ul> <li>Designed reproducible experiment management pipelines covering data processing, training, inference, and result analysis.</li> <li>Automated model evaluation and visualization workflows to support efficient iteration and comparison of generative model variants.</li> </ul> <hr>