Startup Avril

Testing and playing around with VAEs using the mnist dataset

Semi-supervised VAE model for protein localization prediction from microscopy images

Latent Representation and Exploration of Images Using Variational AutoEncoders

These are the results for Focal Frequency Loss for Image Reconstruction and Synthesis paper but with different dataset. In this case the TFW: Annotated Thermal Faces in the Wild Dataset. By using novel focal frequency loss it allows a model to adaptively focus on frequency components that are hard to synthesize by down weighting the easy ones.

Using Beta VAE to test on faces of animal

Deep-Learning surrogate models for various RANS simulations

Stable-Diffusion-Core is a complete implementation of the Stable Diffusion model from scratch. This repository includes the core components necessary for training and deploying diffusion models, including encoders, decoders, CLIP encoding, and U-Net architectures.

This projects were made for the Machine Learning course at the AGH UST in 2024/2025. Obtained maximum grade 5.0.

archived content from probabilistic learning course

Pytorch版本实现的VAE和公式推导

Variational Auto Encoder

implementation of convolutional VAE in pytorch

Reconstructing images using VAE

Quick and dirty test of a VAE on MNIST data.

Implementing several probabilistic generative models in pytorch

Implemented the Variational AutoEnoder and Decoder

This repository is a comprehensive resource for mastering generative AI, featuring in-depth notes and exciting projects. The goal is to stay updated with the latest advancements in generative AI, and explore applications in image & video generation, creative content creation. Explore the limitless possibilities of generative AI today!