An Intuitive Introduction to Generative Adversarial Networks

Abstract

With data becoming increasingly more important in the world of machine learning and data science, researchers have developed systems known to generate data from scratch. These systems are known as Generative Adversarial Networks or GANs. This paper gives a brief and intuitive introduction and analysis over Generative Adversarial Networks and their applications.

Keywords: Generative Adversarial Network (GAN), Synthetic Data, Machine Learning

Introduction to Generative Adversarial Networks

Within the world of machine learning, we often interpolate based on large amounts of data. However, in many areas, data is generally limited. Take for example the COVID-19 crisis in 2020. Many teams built models in order to diagnose the disease; however, because the disease was new, there was very little actual data to create models for diagnosis leading to generally lower accuracies within the models. Generative Adversarial Networks aim to fix this problem.

The essence of GANs is to create data from scratch. With this power, the field of machine learning has the potential to completely be revolutionized. When looking at the name Generative Adversarial Network, one can deduce that there is a generator and an adversary that produces a network. As its name suggests, a GAN is made up of two parts: a generative model and a discriminating model.

The Generative Model

The generative model works through adding noise to a normal distribution. Lets say we are given the function G(z) = x. Where z represents the noise or distortion to a function data set and G(z) is our generative model to produce an image of x. The variable z will generally make changes to different features of our image.

So, for example, if we are given an image of a face, it will take the facial image and alter different features of the image such as the color of eyebrows. Intuitively thinking about this function, by adding a noise, z, to some piece of data, you would be altering one dimension of that piece of data. Therefore, taking facial images as an example, you would be altering one aspect of the face.

#generative-adversarial #machine-learning #computer-science #neural-networks #computer-vision

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An Intuitive Introduction to Generative Adversarial Networks

An Intuitive Introduction to Generative Adversarial Networks

Abstract

With data becoming increasingly more important in the world of machine learning and data science, researchers have developed systems known to generate data from scratch. These systems are known as Generative Adversarial Networks or GANs. This paper gives a brief and intuitive introduction and analysis over Generative Adversarial Networks and their applications.

Keywords: Generative Adversarial Network (GAN), Synthetic Data, Machine Learning

Introduction to Generative Adversarial Networks

Within the world of machine learning, we often interpolate based on large amounts of data. However, in many areas, data is generally limited. Take for example the COVID-19 crisis in 2020. Many teams built models in order to diagnose the disease; however, because the disease was new, there was very little actual data to create models for diagnosis leading to generally lower accuracies within the models. Generative Adversarial Networks aim to fix this problem.

The essence of GANs is to create data from scratch. With this power, the field of machine learning has the potential to completely be revolutionized. When looking at the name Generative Adversarial Network, one can deduce that there is a generator and an adversary that produces a network. As its name suggests, a GAN is made up of two parts: a generative model and a discriminating model.

The Generative Model

The generative model works through adding noise to a normal distribution. Lets say we are given the function G(z) = x. Where z represents the noise or distortion to a function data set and G(z) is our generative model to produce an image of x. The variable z will generally make changes to different features of our image.

So, for example, if we are given an image of a face, it will take the facial image and alter different features of the image such as the color of eyebrows. Intuitively thinking about this function, by adding a noise, z, to some piece of data, you would be altering one dimension of that piece of data. Therefore, taking facial images as an example, you would be altering one aspect of the face.

#generative-adversarial #machine-learning #computer-science #neural-networks #computer-vision

Paper Reading on Generative Adversarial Nets

Generative Adversarial Nets

The main idea is to develop a generative model via an adversarial process. We will discuss what is an adversarial process later. GAN consists of two model. The one is generative model G and the other is discriminative model D. The purpose of a generative model is to generate the closest data as possible for give some input. The purpose of a discriminative model between two classes 0 and 1. 0 meaning the class belongs to Generative output and 1 meaning the class belongs to the true input sample from the original data.

This architecture corresponds to the minmax two-player game. One tries to create conflict over the other. Such networks are called adversarial networks. In the process of creating conflicts, both of them learn to be better and stronger than each other. When the discriminator makes an output of value ½ or 0.5, it implies that the discriminator is not able to distinguish whether the value came from the generator output or the original sample.

Here, the G and D are defined by the multilayered perceptron such that the entire system can be trained with back propagation. The training of the discriminator and generator are done separately.

According to the paper, the generative model can be thought of as analogous to a team of counterfeiters who are trying to produce a fake currency and use them without getting caught.

While, the discriminative model can be thought of as analogous to the Police who are trying to detect the fake currency. Here, both the teams try to improve their methods until the currencies are indistinguishable from the original currency.

Adversarial Networks

Straight from the paper,

To learn the generator’s distribution Pg over data x, we define a prior on input noise variables Pz(z), then represent a mapping to data space as G(z; θg ).

where G is a differentiable function represented by a multilayer perceptron with parameters θ g .

We also define a second multilayer perceptron D(x; θd ) that outputs a single scalar.

Where D(x) represents the probability that x came from the data rather than Pg.

The architecture of GAN can be explained from the following figure.

Image for post

#generative-adversarial #discriminator #adversarial-network #deep-learning #neural-networks

amelia jones

1591340335

How To Take Help Of Referencing Generator

APA Referencing Generator

Many students use APA style as the key citation style in their assignment in university or college. Although, many people find it quite difficult to write the reference of the source. You ought to miss the names and dates of authors. Hence, APA referencing generator is important for reducing the burden of students. They can now feel quite easy to do the assignments on time.

The functioning of APA referencing generator

If you are struggling hard to write the APA referencing then you can take the help of APA referencing generator. It will create an excellent list. You are required to enter the information about the source. Just ensure that the text is credible and original. If you will copy references then it is a copyright violation.

You can use a referencing generator in just a click. It will generate the right references for all the sources. You are required to organize in alphabetical order. The generator will make sure that you will get good grades.

How to use APA referencing generator?

Select what is required to be cited such as journal, book, film, and others. You can choose the type of required citations list and enter all the required fields. The fields are dates, author name, title, editor name, and editions, name of publishers, chapter number, page numbers, and title of journals. You can click for reference to be generated and you will get the desired result.

Chicago Referencing Generator

Do you require the citation style? You can rely on Chicago Referencing Generator and will ensure that you will get the right citation in just a click. The generator is created to provide solutions to students to cite their research paper in Chicago style. It has proved to be the quickest and best citation generator on the market. The generator helps to sort the homework issues in few seconds. It also saves a lot of time and energy.

This tool helps researchers, professional writers, and students to manage and generate text citation essays. It will help to write Chicago style in a fast and easy way. It also provides details and directions for formatting and cites resources.

So, you must stop wasting the time and can go for Chicago Referencing Generator or APA referencing generator. These citation generators will help to solve the problem of citation issues. You can easily create citations by using endnotes and footnotes.

So, you can generate bibliographies, references, in-text citations, and title pages. These are fully automatic referencing style. You are just required to enter certain details about the citation and you will get the citation in the proper and required format.

So, if you are feeling any problem in doing assignment then you can take the help of assignment help.
If you require help for Assignment then livewebtutors is the right place for you. If you see our prices, you will observe that they are actually very affordable. Also, you can always expect a discount. Our team is capable and versatile enough to offer you exactly what you need, the best services for the prices you can afford.

read more:- Are you struggling to write a bibliography? Use Harvard referencing generator

#apa referencing generator #harvard referencing generator #chicago referencing generator #mla referencing generator #deakin referencing generator #oxford referencing generator

How To Create User-Generated Content? [A Simple Guide To Grow Your Brand]

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In this digital world, online businesses aspire to catch the attention of users in a modern and smarter way. To achieve it, they need to traverse through new approaches. Here comes to spotlight is the user-generated content or UGC.

What is user-generated content?
“ It is the content by users for users.”

Generally, the UGC is the unbiased content created and published by the brand users, social media followers, fans, and influencers that highlight their experiences with the products or services. User-generated content has superseded other marketing trends and fallen into the advertising feeds of brands. Today, more than 86 percent of companies use user-generated content as part of their marketing strategy.

In this article, we have explained the ten best ideas to create wonderful user-generated content for your brand. Let’s start without any further ado.

  1. Content From Social Media Platforms
    In the year 2020, there are 3.81 million people actively using social media around the globe. That is the reason social media content matters. Whenever users look at the content on social media that is posted by an individual, then they may be influenced by their content. Perhaps, it can be used to gain more customers or followers on your social media platforms.

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Generally, social media platforms help the brand to generate content for your users. Any user content that promotes your brand on the social media platform is the user-generated content for your business. When users create and share content on social media, they get 28% higher engagement than a standard company post.

Furthermore, you can embed your social media feed on your website also. you can use the Social Stream Designer WordPress plugin that will integrate various social media feeds from different social media platforms like Facebook, Twitter, Instagram, and many more. With this plugin, you can create a responsive wall on your WordPress website or blog in a few minutes. In addition to this, the plugin also provides more than 40 customization options to make your social stream feeds more attractive.

  1. Consumer Survey
    The customer survey provides powerful insights you need to make a better decision for your business. Moreover, it is great user-generated content that is useful for identifying unhappy consumers and those who like your product or service.

In general, surveys can be used to figure out attitudes, reactions, to evaluate customer satisfaction, estimate their opinions about different problems. Another benefit of customer surveys is that collecting outcomes can be quick. Within a few minutes, you can design and load a customer feedback survey and send it to your customers for their response. From the customer survey data, you can find your strengths, weaknesses, and get the right way to improve them to gain more customers.

  1. Run Contests
    A contest is a wonderful way to increase awareness about a product or service. Contest not just helps you to enhance the volume of user-generated content submissions, but they also help increase their quality. However, when you create a contest, it is important to keep things as simple as possible.

Additionally, it is the best way to convert your brand leads to valuable customers. The key to running a successful contest is to make sure that the reward is fair enough to motivate your participation. If the product is relevant to your participant, then chances are they were looking for it in the first place, and giving it to them for free just made you move forward ahead of your competitors. They will most likely purchase more if your product or service satisfies them.

Furthermore, running contests also improve the customer-brand relationship and allows more people to participate in it. It will drive a real result for your online business. If your WordPress website has Google Analytics, then track contest page visits, referral traffic, other website traffic, and many more.

  1. Review And Testimonials
    Customer reviews are a popular user-generated content strategy. One research found that around 68% of customers must see at least four reviews before trusting a brand. And, approximately 40 percent of consumers will stop using a business after they read negative reviews.

The business reviews help your consumers to make a buying decision without any hurdle. While you may decide to remove all the negative reviews about your business, those are still valuable user-generated content that provides honest opinions from real users. Customer feedback can help you with what needs to be improved with your products or services. This thing is not only beneficial to the next customer but your business as a whole.

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Reviews are powerful as the platform they are built upon. That is the reason it is important to gather reviews from third-party review websites like Google review, Facebook review, and many more, or direct reviews on a website. It is the most vital form of feedback that can help brands grow globally and motivate audience interactions.

However, you can also invite your customers to share their unique or successful testimonials. It is a great way to display your products while inspiring others to purchase from your website.

  1. Video Content
    A great video is a video that is enjoyed by visitors. These different types of videos, such as 360-degree product videos, product demo videos, animated videos, and corporate videos. The Facebook study has demonstrated that users spend 3x more time watching live videos than normal videos. With the live video, you can get more user-created content.

Moreover, Instagram videos create around 3x more comments rather than Instagram photo posts. Instagram videos generally include short videos posted by real customers on Instagram with the tag of a particular brand. Brands can repost the stories as user-generated content to engage more audiences and create valid promotions on social media.

Similarly, imagine you are browsing a YouTube channel, and you look at a brand being supported by some authentic customers through a small video. So, it will catch your attention. With the videos, they can tell you about the branded products, especially the unboxing videos displaying all the inside products and how well it works for them. That type of video is enough to create a sense of desire in the consumers.

Continue Reading

#how to get more user generated content #importance of user generated content #user generated content #user generated content advantages #user generated content best practices #user generated content pros and cons

Marlon  Boyle

Marlon Boyle

1594312560

Autonomous Driving Network (ADN) On Its Way

Talking about inspiration in the networking industry, nothing more than Autonomous Driving Network (ADN). You may hear about this and wondering what this is about, and does it have anything to do with autonomous driving vehicles? Your guess is right; the ADN concept is derived from or inspired by the rapid development of the autonomous driving car in recent years.

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Driverless Car of the Future, the advertisement for “America’s Electric Light and Power Companies,” Saturday Evening Post, the 1950s.

The vision of autonomous driving has been around for more than 70 years. But engineers continuously make attempts to achieve the idea without too much success. The concept stayed as a fiction for a long time. In 2004, the US Defense Advanced Research Projects Administration (DARPA) organized the Grand Challenge for autonomous vehicles for teams to compete for the grand prize of $1 million. I remembered watching TV and saw those competing vehicles, behaved like driven by drunk man, had a really tough time to drive by itself. I thought that autonomous driving vision would still have a long way to go. To my surprise, the next year, 2005, Stanford University’s vehicles autonomously drove 131 miles in California’s Mojave desert without a scratch and took the $1 million Grand Challenge prize. How was that possible? Later I learned that the secret ingredient to make this possible was using the latest ML (Machine Learning) enabled AI (Artificial Intelligent ) technology.

Since then, AI technologies advanced rapidly and been implemented in all verticals. Around the 2016 time frame, the concept of Autonomous Driving Network started to emerge by combining AI and network to achieve network operational autonomy. The automation concept is nothing new in the networking industry; network operations are continually being automated here and there. But this time, ADN is beyond automating mundane tasks; it reaches a whole new level. With the help of AI technologies and other critical ingredients advancement like SDN (Software Defined Network), autonomous networking has a great chance from a vision to future reality.

In this article, we will examine some critical components of the ADN, current landscape, and factors that are important for ADN to be a success.

The Vision

At the current stage, there are different terminologies to describe ADN vision by various organizations.
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Even though slightly different terminologies, the industry is moving towards some common terms and consensus called autonomous networks, e.g. TMF, ETSI, ITU-T, GSMA. The core vision includes business and network aspects. The autonomous network delivers the “hyper-loop” from business requirements all the way to network and device layers.

On the network layer, it contains the below critical aspects:

  • Intent-Driven: Understand the operator’s business intent and automatically translate it into necessary network operations. The operation can be a one-time operation like disconnect a connection service or continuous operations like maintaining a specified SLA (Service Level Agreement) at the all-time.
  • **Self-Discover: **Automatically discover hardware/software changes in the network and populate the changes to the necessary subsystems to maintain always-sync state.
  • **Self-Config/Self-Organize: **Whenever network changes happen, automatically configure corresponding hardware/software parameters such that the network is at the pre-defined target states.
  • **Self-Monitor: **Constantly monitor networks/services operation states and health conditions automatically.
  • Auto-Detect: Detect network faults, abnormalities, and intrusions automatically.
  • **Self-Diagnose: **Automatically conduct an inference process to figure out the root causes of issues.
  • **Self-Healing: **Automatically take necessary actions to address issues and bring the networks/services back to the desired state.
  • **Self-Report: **Automatically communicate with its environment and exchange necessary information.
  • Automated common operational scenarios: Automatically perform operations like network planning, customer and service onboarding, network change management.

On top of those, these capabilities need to be across multiple services, multiple domains, and the entire lifecycle(TMF, 2019).

No doubt, this is the most ambitious goal that the networking industry has ever aimed at. It has been described as the “end-state” and“ultimate goal” of networking evolution. This is not just a vision on PPT, the networking industry already on the move toward the goal.

David Wang, Huawei’s Executive Director of the Board and President of Products & Solutions, said in his 2018 Ultra-Broadband Forum(UBBF) keynote speech. (David W. 2018):

“In a fully connected and intelligent era, autonomous driving is becoming a reality. Industries like automotive, aerospace, and manufacturing are modernizing and renewing themselves by introducing autonomous technologies. However, the telecom sector is facing a major structural problem: Networks are growing year by year, but OPEX is growing faster than revenue. What’s more, it takes 100 times more effort for telecom operators to maintain their networks than OTT players. Therefore, it’s imperative that telecom operators build autonomous driving networks.”

Juniper CEO Rami Rahim said in his keynote at the company’s virtual AI event: (CRN, 2020)

“The goal now is a self-driving network. The call to action is to embrace the change. We can all benefit from putting more time into higher-layer activities, like keeping distributors out of the business. The future, I truly believe, is about getting the network out of the way. It is time for the infrastructure to take a back seat to the self-driving network.”

Is This Vision Achievable?

If you asked me this question 15 years ago, my answer would be “no chance” as I could not imagine an autonomous driving vehicle was possible then. But now, the vision is not far-fetch anymore not only because of ML/AI technology rapid advancement but other key building blocks are made significant progress, just name a few key building blocks:

  • software-defined networking (SDN) control
  • industry-standard models and open APIs
  • Real-time analytics/telemetry
  • big data processing
  • cross-domain orchestration
  • programmable infrastructure
  • cloud-native virtualized network functions (VNF)
  • DevOps agile development process
  • everything-as-service design paradigm
  • intelligent process automation
  • edge computing
  • cloud infrastructure
  • programing paradigm suitable for building an autonomous system . i.e., teleo-reactive programs, which is a set of reactive rules that continuously sense the environment and trigger actions whose continuous execution eventually leads the system to satisfy a goal. (Nils Nilsson, 1996)
  • open-source solutions

#network-automation #autonomous-network #ai-in-network #self-driving-network #neural-networks