A unified audio-video variational autoencoder with cross-modal alignment for joint reconstruction and generation
The AEGIS-128X and AEGIS-256X high performance ciphers.
Official PyTorch implementation of A Quaternion-Valued Variational Autoencoder (QVAE).
This repository provides implementation simplified Variational Autoencoder (VAE), producing smooth latent space completely unsupervised manner. And this can be used as generative model as well.
Interactive demonstration of synthetic data generation using GANs and VAEs with statistical comparison
Use a VAE to generate all new pokemons
A zero-dependency C++17 AES encryption library supporting AES-128, AES-192, and AES-256 in ECB, CBC, CTR, and GCM modes, with SIMD-accelerated backends and runtime CPU dispatch
This repository offers a comprehensive collection of resources, tutorials, and examples focused on generative AI. It covers various generative models, including GANs, VAEs, and transformers, with code examples, pre-trained models, and datasets for practical implementation.
Every Variational Autoencoder that I have encountered in Keras
Generative deep learning models implemented from scratch in PyTorch, each paired with its mathematical derivation.
This repository contains all tasks, notebooks, and projects from my AI L3 internship at Orange Digital Center, including NLP, Computer Vision, GANs, VAEs, and MLOps using tools like BERT, YOLOv8, and Docker.
VAE model for the design of record breaking single molecule magnets with applications in spintronics and qubits.
AES-NI–accelerated duplex/sponge in header-only C++23 — hashing, MAC, XOF, and PRNG, with a Keccak-style flat sponge claim and a fast multicore tree hash.
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