Applying Generative Adversarial Network to generate novel 3D images

In this post, I’ll demonstrate the behavior of Generative Adversarial Networks (GANs) on 3D images and how it can help to generate novel 3D images.

To start with, I’ll divide this post into the following sections:

1. Problem Statement
2. Introduction
3. Proposed Solution
4. Code
5. Results
6. Observations
7. Limitations
8. Future Scope
9. Acknowledgement

Problem Statement

Three-dimensional (3D) models have become popular because of their variety of applications in the domains of industrial product design, cultural relics restoration, medical diagnosis, 3D games, and so on. The traditional way of designing and constructing 3D models is very complicated, which hampers ordinary users’ enthusiasm for creative design and the satisfaction of 3D models that meet their requirements. The modern way of designing and constructing 3D models involves usage of some of the popular 3D modeling software like NX, CATIA, SolidWorks or 3D scanners to obtain digital 3D models. However, it is generally a very exhaustive task to quickly develop some creative or innovative design for an existing 3D model. Therefore, exploring effective 3D image generation methods is an essential aspect in the domain of computer graphics and computer vision.

To address the above requirement of generating novel 3D images, I’ve applied traditional generative adversarial network (GAN) with the introduction of three different class of networks, i.e., convolutional neural networks (CNN), capsule networks (CapsNet) and auto-encoding ability on the NORB dataset (NYU Object Recognition Benchmark).

Introduction

NORB dataset

The smallNORB dataset is considered as a staple dataset for testing the efficacy of generative models in the 3D domain. This dataset has gray-level stereo images of 5 classes of toys: airplanes, cars, trucks, humans, and animals. There are 10 physical instances of each class. 5 physical instances of a class are selected for the training data and the other 5 for the test data. Every individual toy is pictured at 18 different azimuths (0–340), 9 elevations, and 6 lighting conditions, so the training and test set each contain 24,300 stereo pairs of 96x96 images.

#generative-adversarial #machine-learning #deep-learning #3d-images #experimental #deep learning

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Applying Generative Adversarial Network to generate novel 3D images

In this post, I’ll demonstrate the behavior of Generative Adversarial Networks (GANs) on 3D images and how it can help to generate novel 3D images.

To start with, I’ll divide this post into the following sections:

1. Problem Statement
2. Introduction
3. Proposed Solution
4. Code
5. Results
6. Observations
7. Limitations
8. Future Scope
9. Acknowledgement

Problem Statement

Three-dimensional (3D) models have become popular because of their variety of applications in the domains of industrial product design, cultural relics restoration, medical diagnosis, 3D games, and so on. The traditional way of designing and constructing 3D models is very complicated, which hampers ordinary users’ enthusiasm for creative design and the satisfaction of 3D models that meet their requirements. The modern way of designing and constructing 3D models involves usage of some of the popular 3D modeling software like NX, CATIA, SolidWorks or 3D scanners to obtain digital 3D models. However, it is generally a very exhaustive task to quickly develop some creative or innovative design for an existing 3D model. Therefore, exploring effective 3D image generation methods is an essential aspect in the domain of computer graphics and computer vision.

To address the above requirement of generating novel 3D images, I’ve applied traditional generative adversarial network (GAN) with the introduction of three different class of networks, i.e., convolutional neural networks (CNN), capsule networks (CapsNet) and auto-encoding ability on the NORB dataset (NYU Object Recognition Benchmark).

Introduction

NORB dataset

The smallNORB dataset is considered as a staple dataset for testing the efficacy of generative models in the 3D domain. This dataset has gray-level stereo images of 5 classes of toys: airplanes, cars, trucks, humans, and animals. There are 10 physical instances of each class. 5 physical instances of a class are selected for the training data and the other 5 for the test data. Every individual toy is pictured at 18 different azimuths (0–340), 9 elevations, and 6 lighting conditions, so the training and test set each contain 24,300 stereo pairs of 96x96 images.

#generative-adversarial #machine-learning #deep-learning #3d-images #experimental #deep learning

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.

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

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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.

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Image Super-Resolution Using Generative Adversarial Networks

In this post, we’re going to investigate the field of image super-resolution and its applications in real world. We’ll discuss a brilliant state-of-the-art model involving generative adversarial networks (GANs) for this task and try to understand the underlying logic behind the approach.

So let’s jump right in!

Kindly refer to the link given below for the complete research paper. The model discussed in this post will be based on the approach used in this paper.

Research Paper Link

#generative-adversarial #machine-learning #image-processing #super-resolution #heartbeat