Neural Injective Geometry networks (NIGnets) for non-self-intersecting geometry.
- Add .gitignore for the project.
- Create first cut documentation pages using Jupyterbooks and MyST markdown.
- Motivation for non-self-intersecting geometry.
- Add .gitignore for MyST markdown.
- Launch web page for documentation using github pages.
- Create Injective Networks.
- Basic architecture.
- Generate proper documentation.
- Proper docstrings. Follow Google python coding style guide and numpy style guide.
- Use math equations.
- Use type annotations.
- Impossible intersection using matrix exponential.
- Use geosimilarity for loss functions.
- Add testing code.
- Create automated training function.
- Create plot function. Parameterized and target shape comparison.
- Generate a bunch of target shapes. Use SVGs.
- Update documentation with Injective Networks and showcase.
- Add documentation on Injective Networks.
- Fit basic shapes using Injective Networks.
- Create showcase for Injective Networks.
- Create showcase for Injective Networks with impossible intersection.
- Add license.
- Create logos.
- Create logo.
- Create favicon.
- Use on website.
- Add Monotonic networks.
- Add Min-Max nets.
- Add Smooth Min-Max nets.
- Add documentation for Monotonic Nets.
- Add showcase for Monotonic Nets.
- Create ResNet-like architecture using skip connections.
- Add skip connections that preserve injectivity.
- Add showcase for ResNet architecture.
- Add Auxilliary networks.
- Add Pre-Aux nets.
- Add Post-Aux nets.
- Add documentation for Aux Nets.
- Add showcase for Pre and Post nets separately and combined.
- Fit repeating and fractal shapes.
- Use trignometric activations in Pre-Aux networks.
- Showcase for the full architecture.
- 3D NIGnets.
- Create documentation for 3D NIGnets.
- Create 3D surface point clouds to fit to.
- Fit 3D geometric shapes for showcase.
- Experiment with different geometric loss functions.