DD2402 Advanced Individual Course in Computational Biology Project
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Updated
Nov 9, 2023 - Python
Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the Discriminator network and the Generator network. GANs have been shown to be powerful generative models and are able to successfully generate new data given a large enough training dataset.
DD2402 Advanced Individual Course in Computational Biology Project
The repository contains software library for Data Augmentation Services
Various Preprocessing tools for use with Generative Adversarial Networks
[iScience] [Tensorflow] Subcellular Signal Segmenting Spatiotemporal Model.
PyTorch Implementation of Deep Convolutional Generative Adversarial Networks (DCGAN)
Implementation of "Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Learning" (JASA, 2023+)
Implementation of my research project 'Conditional Generaton of Aerial Images for Imbalanced Learning using Generative Adversarial Networks'.
Project to transform a natural language description into an image using Generative Adversarial Networks.
Globally and Locally Consistent Image Completion using CNNs and GANs
Generative Dog Images (Kaggle Competition)
Pytorch Implementation of Generative Adversarial Nets (GAN)
DeepLearning-Visual Recognition
Implementations of different Generative Adversarial Networks
A Generative Adversarial Network implementation that generates Sharingans
neural networks GAN sandbox
Demo for implementation of Generative Adversarial Networks (GANs)
This is the code for generating new designs of shoes using the concept of generative adversarial networks.
Implementation of Cycle-consistent Generative Adversarial Networks for Image-to-Image Translation in Keras
A Tensorflow-layer API Implementation of Deep Generative Models (MNIST Examples)
Released June 10, 2014