Deep CNN for performing 3D super resolution on CT/MRI scans
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Updated
Jul 10, 2024 - Python
Deep CNN for performing 3D super resolution on CT/MRI scans
This repository encompasses a comprehensive research of Generative Adversarial Networks (GANs) for Biomaterial Discovery. Our research delves into the generation of intricate biomaterial topographies through the innovative application of AI/ML techniques. Discover our findings, code implementations and datasets in this repository!
A template repository for GANs
The GAN Book: Train stable Generative Adversarial Networks using TensorFlow2, Keras and Python.
A collections of basic autoencoders and Generative models for chemistry
PyTorch implementation of 'PGGAN' (Karras et al., 2018) from scratch and training it on CelebA-HQ at 512 × 512
This repo contains the implementation of various generative adversarial networks for generating fake handwritten digits.
Labs for Generative Deep Learning with TensorFlow by DeepLearning.AI on Coursera
The Generative Adversarial Networks with Python would serve as our primary reference throughout the project. The models would be trained on the MNIST dataset. The official TensorFlow framework and documentation will be used to implement the different architectures on Python. These papers would be used to implement various evaluation met
Repository process and positioning from init dataset
Developed a music generation deep learning model using WGAN-GP and self-attention, aimed at creating melodic compositions.
An implementation of one personal project to gain experience with Generative Adversarial Network models and in particular on Wasserstrein GAN with gradient penalty. The final application's purpose is to generate synthetic images given a food category.
Implementations of GANs
Road towards diffusion models.
My own GAN implementation (WGAN-GP with Pytorch)
Machine learning, in numpy
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