I wrote a notebook for a lecture/exercice on image generation with flow matching. The idea is to use FM to render images composed of simple shapes using their attributes (type, size, color, etc). Not super useful but fun and easy to train!
colab.research.google.com/drive/16GJyb...
Comments welcome!
I'm slowly putting my intro to ML course material on github, starting with the lab sessions: github.com/davidpicard/...
These are self-contained notebooks in which you have to implement famous algorithms from the literature (k-NN, SVM, DT, etc), with a custom dataset that I (painstakingly) made!
🍏 New preprint alert! 🍏
PoM: Efficient Image and Video Generation with the Polynomial Mixer
arxiv.org/abs/2411.12663
This is my latest "summer project" and it was so big I had to call in reinforcements (Thanks @nicolasdufour.bsky.social)
TL;DR Transformers are for boomers, welcome to the future
🧵👇
arxiv.org
Diffusion models based on Multi-Head Attention (MHA) have become ubiquitous to generate high quality images and videos. However, encoding an image or a video as a sequence of patches results in costly...
1/n🚀Gaussians > Differentiable function > Mesh?
Check out our new work: MILo: Mesh-In-the-Loop Gaussian Splatting!
🎉Accepted to SIGGRAPH Asia 2025 (TOG)
MILo is a novel differentiable framework that extracts meshes directly from Gaussian parameters during training.
🧵👇
Guillaume Astruc, Nicolas Gonthier, Clement Mallet, Loic Landrieu
AnySat: An Earth Observation Model for Any Resolutions, Scales, and Modalities
https://arxiv.org/abs/2412.14123
