Build notes · Goh Kun Ming
Handwritten Character Lab
AI · Notebook experiment
Jul 2025 – Aug 2025
Project overview
Comparing the ways neural networks create.
A generative-modelling lab for 16 selected EMNIST Letters classes, preserving coursework notebooks alongside reusable Python tools. It explores eight GAN families and variational or conditional extensions. Evaluation tools are provided, but committed checkpoints and multi-seed results do not establish a winning model.
Inside this build
- Configured consistent grayscale inputs, latent vectors and reproducible experiment settings.
- Implemented eight GAN families alongside variational and conditional extensions.
- Added FID, KID, diversity, mode-collapse and embedding-analysis tools.
- The LinkedIn project description reports validating sample counts, finite values, dimensions and covariance structure before computing metrics.
- Documented limitations of natural-image feature extractors for small handwritten letters.
- Preserved the original notebook and output-free research sections with preprocessing and integrity tests.