- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Which Face is Real? Applying StyleGAN to Create Fake People - May 1, 2020.
This post explains using a pre-trained GAN to generate human faces, and discusses the most common generative pitfalls associated with doing so.
- 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.
- 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.
- Large Scale Adversarial Representation Learning - Feb 7, 2020.
GANs can be used for unsupervised learning where a generator maps latent samples to generate data, but this framework does not include an inverse mapping from data to latent representation. BiGAN adds an encoder E to the standard generator-discriminator GAN architecture — the encoder takes input data x and outputs a latent representation z of the input.
- 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.
- 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.
- 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.
- 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.
- 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.
- Boost Your Image Classification Model - May 27, 2019.
Check out this collection of tricks to improve the accuracy of your classifier.
- KDnuggets™ News 19:n17, May 1: The most desired skill in data science; Seeking KDnuggets Editors, work remotely - May 1, 2019.
This week, find out about the most desired skill in data science, learn which projects to include in your portfolio, identify a single strategy for pulling data from a Pandas DataFrame (once and for all), read the results of our Top Data Science and Machine Learning Methods poll, and much more.
- 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.
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- 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.
- KDnuggets™ News 19:n16, Apr 24: Data Visualization in Python with Matplotlib & Seaborn; Getting Into Data Science: The Ultimate Q&A - Apr 24, 2019.
Best Data Visualization Techniques for small and large data; The Rise of Generative Adversarial Networks; Approach pre-trained deep learning models with caution; How Optimization Works; Building a Flask API to Automatically Extract Named Entities Using SpaCy
- 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.
- 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.
- 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.
- 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.
- KDnuggets™ News 18:n41, Oct 31: Introduction to Deep Learning with Keras; Easy Named Entity Recognition with Scikit-Learn - Oct 31, 2018.
Also: Generative Adversarial Networks - Paper Reading Road Map; How I Learned to Stop Worrying and Love Uncertainty; Implementing Automated Machine Learning Systems with Open Source Tools; Notes on Feature Preprocessing: The What, the Why, and the How
- 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.
- KDnuggets™ News 18:n30, Aug 8: Iconic Data Visualisation; Data Scientist Interviews Demystified; Simple Statistics in Python - Aug 8, 2018.
Also: Selecting the Best Machine Learning Algorithm for Your Regression Problem; From Data to Viz: how to select the the right chart for your data; Only Numpy: Implementing GANs and Adam Optimizer using Numpy; Programming Best Practices for Data Science
- 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.
- Generative Adversarial Neural Networks: Infinite Monkeys and The Great British Bake Off - May 22, 2018.
Adversarial Neural Networks are oddly named since they actually cooperate to make things.
- 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.
- The New Neural Internet is Coming - Feb 23, 2018.
The Generative Adversarial Networks (GANs) are the first step of neural networks technology learning creativity.
- Age of AI Conference 2018 – Day 1 Highlights - Feb 21, 2018.
Here are some of the highlights from the first day of the Age of AI Conference, January 31, at the Regency Ballroom in San Francisco.
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- Generative Adversarial Networks, an overview - Jan 15, 2018.
In this article, we’ll explain GANs by applying them to the task of generating images. One of the few successful techniques in unsupervised machine learning, and are quickly revolutionizing our ability to perform generative tasks.
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- InfoGAN - Generative Adversarial Networks Part III - Nov 30, 2017.
In this third part of this series of posts the contributions of InfoGAN will be explored, which apply concepts from Information Theory to transform some of the noise terms into latent codes that have systematic, predictable effects on the outcome.
- Generative Adversarial Networks — Part II - Nov 17, 2017.
Second part of this incredible overview of Generative Adversarial Networks, explaining the contributions of Deep Convolutional-GAN (DCGAN) paper.
- Overview of GANs (Generative Adversarial Networks) – Part I - Nov 10, 2017.
A great introductory and high-level summary of Generative Adversarial Networks.
- CAN (Creative Adversarial Network) - Explained - Jul 12, 2017.
GANs (Generative Adversarial Networks), a type of Deep Learning networks, have been very successful in creating non-procedural content. This work explores the possibility of machine generated creative content.
- Top /r/MachineLearning Posts, April: Why Momentum Really Works; Machine Learning with Scikit-Learn & TensorFlow - May 5, 2017.
Why Momentum Really Works; O'Reilly's Hands-On Machine Learning with Scikit-Learn and TensorFlow; Implemented BEGAN and saw a cute face at iteration 168k; Self-driving car course; Exploring the mysteries of Go; DeepMind Solves AGI
- 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 Stubborn Networks – A Breakthrough Advance Towards Adversarial Machine Intelligence - Apr 1, 2017.
The exciting announcement yesterday of Deep Stubborn Networks (StubNets) introduces an innovative refinement to GANs, taking their development in a new direction.
- Deep Learning, Generative Adversarial Networks & Boxing – Toward a Fundamental Understanding - Mar 28, 2017.
In this post we will see why GANs have so much potential, and frame GANs as a boxing match between two opponents.
- 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.
- The Major Advancements in Deep Learning in 2016 - Jan 5, 2017.
Get a concise overview of the major advancements observed in deep learning over the past year.
- 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 Research Review: Generative Adversarial Nets - Oct 31, 2016.
This edition of Deep Learning Research Review explains recent research papers in the deep learning subfield of Generative Adversarial Networks. Don't have time to read some of the top papers? Get the overview here.
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- 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.
- 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?
- Top 5 Deep Learning Resources, January - Jan 7, 2016.
There is an increasing volume of deep learning research, articles, blog posts, and news constantly emerging. Our Deep Learning Reading List aims to make this information easier to digest.
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