Generative Adversarial Network (29)

• An overview of synthetic data types and generation methods - Feb 22, 2021.
  
  Synthetic data can be used to test new products and services, validate models, or test performances because it mimics the statistical property of production data. Today you'll find different types of structured and unstructured synthetic data.

  Autoencoder, GANs, Generative Adversarial Network, Synthetic Data

• Adversarial generation of extreme samples - Feb 2, 2021.
  
  In order to mitigate risks when modelling extreme events, it is vital to be able to generate a wide range of extreme, and realistic, scenarios. Researchers from the National University of Singapore and IIT Bombay have developed an approach to do just that.

  AI, GANs, Generative Adversarial Network, Generative Models, Sampling

• Machine learning adversarial attacks are a ticking time bomb - Jan 29, 2021.
  
  Software developers and cyber security experts have long fought the good fight against vulnerabilities in code to defend against hackers. A new, subtle approach to maliciously targeting machine learning models has been a recent hot topic in research, but its statistical nature makes it difficult to find and patch these so-called adversarial attacks. Such threats in the real-world are becoming imminent as the adoption of machine learning spreads, and a systematic defense must be implemented.

  Adversarial, Generative Adversarial Network, Machine Learning

• Deep Learning for Virtual Try On Clothes – Challenges and Opportunities - Oct 16, 2020.
  
  Learn about the experiments by MobiDev for transferring 2D clothing items onto the image of a person. As part of their efforts to bring AR and AI technologies into virtual fitting room development, they review the deep learning algorithms and architecture under development and the current state of results.

  Computer Vision, Deep Learning, Fashion, Generative Adversarial Network, Generative Models, Humans, Image Generation

• The Insiders’ Guide to Generative and Discriminative Machine Learning Models - Sep 18, 2020.
  
  In this article, we will look at the difference between generative and discriminative models, how they contrast, and one another.

  Deep Learning, GANs, Generative Adversarial Network, Modeling

• Microsoft Research Unveils Three Efforts to Advance Deep Generative Models - May 4, 2020.
  
  Optimus, FQ-GAN and Prevalent bring new ideas to apply generative models at large scale.

  GANs, Generative Adversarial Network, Microsoft, Microsoft Research

• Build an app to generate photorealistic faces using TensorFlow and Streamlit - Apr 7, 2020.
  
  We’ll show you how to quickly build a Streamlit app to synthesize celebrity faces using GANs, Tensorflow, and st.cache.

  App, GANs, Generative Adversarial Network, Python, Streamlit, TensorFlow

• Generate Realistic Human Face using GAN - Mar 10, 2020.
  
  This article contain a brief intro to Generative Adversarial Network(GAN) and how to build a Human Face Generator.

  GANs, Generative Adversarial Network, Neural Networks, Python

• Semi-supervised learning with Generative Adversarial Networks - Jan 24, 2020.
  
  The paper discussed in this post, Semi-supervised learning with Generative Adversarial Networks, utilizes a GAN architecture for multi-label classification.

  GANs, Generative Adversarial Network, Supervised Learning

• Uber Creates Generative Teaching Networks to Better Train Deep Neural Networks - Jan 13, 2020.
  
  The new technique can really improve how deep learning models are trained at scale.

  Generative Adversarial Network, Neural Networks, Training, Uber

• Intro to Adversarial Machine Learning and Generative Adversarial Networks - Oct 23, 2019.
  
  In this crash course on GANs, we explore where they fit into the pantheon of generative models, how they've changed over time, and what the future has in store for this area of machine learning.

  Adversarial, AI, GANs, Generative Adversarial Network, Machine Learning

• An Overview of Density Estimation - Oct 14, 2019.
  
  Density estimation is estimating the probability density function of the population from the sample. This post examines and compares a number of approaches to density estimation.

  Generative Adversarial Network, Probability, Statistics

• A Summary of DeepMind’s Protein Folding Upset at CASP13 - Jul 17, 2019.
  
  Learn how DeepMind dominated the last CASP competition for advancing protein folding models. Their approach using gradient descent is today's state of the art for predicting the 3D structure of a protein knowing only its comprising amino acid compounds.

  Bioinformatics, Deep Learning, DeepMind, Exxact, Generative Adversarial Network, Gradient Descent, Protein

• Boost Your Image Classification Model - May 27, 2019.
  
  Check out this collection of tricks to improve the accuracy of your classifier.

  fast.ai, Generative Adversarial Network, Image Classification, Image Recognition, Python

• Graduating in GANs: Going From Understanding Generative Adversarial Networks to Running Your Own - Apr 25, 2019.
  
  Read how generative adversarial networks (GANs) research and evaluation has developed then implement your own GAN to generate handwritten digits.

  Pages: 1 2

  Deep Learning, GANs, Generative Adversarial Network, Generative Models, MNIST, Neural Networks, Python

• Generative Adversarial Networks – Key Milestones and State of the Art - Apr 24, 2019.
  
  We provide an overview of Generative Adversarial Networks (GANs), discuss challenges in GANs learning, and examine two promising GANs: the RadialGAN, designed for numbers, and the StyleGAN, which does style transfer for images.

  GANs, Generative Adversarial Network, NVIDIA

• The Rise of Generative Adversarial Networks - Apr 19, 2019.
  
  A comprehensive overview of Generative Adversarial Networks, covering its birth, different architectures including DCGAN, StyleGAN and BigGAN, as well as some real-world examples.

  Art, Deepfakes, GANs, Generative Adversarial Network, Ian Goodfellow

• Which Face is Real? - Apr 2, 2019.
  
  Which Face Is Real? was developed based on Generative Adversarial Networks as a web application in which users can select which image they believe is a true person and which was synthetically generated. The person in the synthetically generated photo does not exist.

  Deep Learning, GANs, Generative Adversarial Network, Neural Networks, NVIDIA, Python

• My favorite mind-blowing Machine Learning/AI breakthroughs - Mar 14, 2019.
  
  We present some of our favorite breakthroughs in Machine Learning and AI in recent times, complete with papers, video links and brief summaries for each.

  AI, AlphaStar, GANs, Generative Adversarial Network, Machine Learning, Machine Translation, Reinforcement Learning, Robots

• State of Deep Learning and Major Advances: H2 2018 Review - Dec 13, 2018.
  
  In this post we summarise some of the key developments in deep learning in the second half of 2018, before briefly discussing the road ahead for the deep learning community.

  Deep Learning, Generative Adversarial Network, NLP, PyTorch, TensorFlow, Trends

• Generative Adversarial Networks – Paper Reading Road Map - Oct 24, 2018.
  
  To help the others who want to learn more about the technical sides of GANs, I wanted to share some papers I have read in the order that I read them.

  GANs, Generative Adversarial Network, Neural Networks

• Only Numpy: Implementing GANs and Adam Optimizer using Numpy - Aug 6, 2018.
  
  This post is an implementation of GANs and the Adam optimizer using only Python and Numpy, with minimal focus on the underlying maths involved.

  GANs, Generative Adversarial Network, Neural Networks, numpy, Optimization, Python

• GANs in TensorFlow from the Command Line: Creating Your First GitHub Project - May 16, 2018.
  
  In this article I will present the steps to create your first GitHub Project. I will use as an example Generative Adversarial Networks.

  GANs, Generative Adversarial Network, GitHub, Neural Networks, Python, Rubens Zimbres, TensorFlow

• Overview of GANs (Generative Adversarial Networks) – Part I - Nov 10, 2017.
  
  A great introductory and high-level summary of Generative Adversarial Networks.

  Deep Learning, GANs, Generative Adversarial Network, Neural Networks

• More Deep Learning “Magic”: Paintings to photos, horses to zebras, and more amazing image-to-image translation - Apr 17, 2017.
  
  This is an introduction to recent research which presents an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples.

  Deep Learning, Generative Adversarial Network, Generative Models, Torch

• 6 areas of AI and Machine Learning to watch closely - Jan 25, 2017.
  
  Artificial Intelligence is a generic term and many fields of science overlaps when comes to make an AI application. Here is an explanation of AI and its 6 major areas to be focused, going forward.

  AI, Deep Neural Network, Generative Adversarial Network, Machine Learning, Reinforcement Learning

• Generative Adversarial Networks – Hot Topic in Machine Learning - Jan 3, 2017.
  
  What is Generative Adversarial Networks (GAN) ? A very illustrative explanation of GAN is presented here with simple examples like predicting next frame in video sequence or predicting next word while typing in google search.

  Deep Learning, Generative Adversarial Network, Machine Learning, Neural Networks, NIPS

• Yann LeCun Quora Session Overview - Aug 1, 2016.
  
  Here is a quick oversight, with excerpts, of the Yann LeCun Quora Session which took place on Thursday July 28, 2016.

  Deep Learning, Generative Adversarial Network, Quora, Yann LeCun

• Are Deep Neural Networks Creative? - May 12, 2016.
  
  Deep neural networks routinely generate images and synthesize text. But does this amount to creativity? Can we reasonably claim that deep learning produces art?

  Artificial Intelligence, Deep Learning, Generative Adversarial Network, Generative Models, Recurrent Neural Networks, Reinforcement Learning, Zachary Lipton