Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images. The GAN architecture is comprised of both a generator and a discriminator model. The generator is responsible for creating new outputs, such as images, that plausibly could have come from the original dataset. The generator model is typically implemented using a deep convolutional neural network and results-specialized layers that learn to fill in features in an image rather than extract features from an input image. Two common types of layers that can be used in the generator model are a upsample layer (UpSampling2D) that simply doubles the dimensions of the input and the transpose convolutional layer (Conv2DTranspose) that performs an inverse convolution operation. In this tutorial, you will discover how to use UpSampling2D and Conv2DTranspose Layers in Generative Adversarial Networks when generating images. Discover how to develop DCGANs, conditional GANs, Pix2Pix, CycleGANs, and more with Keras in my new GANs book, with 29 step-by-step tutorials and full source code.
Convolutional nets can be used to classify images (name what they see), cluster them by similarity (photo search), and perform object recognition within scenes. They can identify faces, individuals, street signs, eggplants, platypuses and many other aspects of visual data. Convolutional nets overlap with text analysis via optical character recognition (OCR), where the images are symbols to be transcribed, and they can also be applied to sound when it is represented visually. The efficacy of convolutional nets (ConvNets or CNNs) in image recognition is one of the main reasons why the world has woken up to deep learning. They are powering major advances in machine vision, which has obvious applications for self-driving cars, robotics, drones, and treatments for the visually impaired.
A Convolutional neural network (CNN) is a neural network that has one or more convolutional layers and are used mainly for image processing, classification, segmentation and also for other auto correlated data. A convolution is essentially sliding a filter over the input. One helpful way to think about convolutions is this quote from Dr Prasad Samarakoon: "A convolution can be thought as "looking at a function's surroundings to make better/accurate predictions of its outcome." Rather than looking at an entire image at once to find certain features it can be more effective to look at smaller portions of the image. The most common use for CNNs is image classification, for example identifying satellite images that contain roads or classifying hand written letters and digits.
Perhaps you've wondered how Facebook or Instagram is able to automatically recognize faces in an image, or how Google lets you search the web for similar photos just by uploading a photo of your own. These features are examples of computer vision, and they are powered by convolutional neural networks (CNNs). Yet what exactly are convolutional neural networks? Let's take a deep dive into the architecture of a CNN and understand how they operate. Before we begin talking about convolutional neural networks, let's take a moment to define regular neural networks.
What are Convolutional Neural Networks and why are they important? Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects and traffic signs apart from powering vision in robots and self driving cars. In Figure 1 above, a ConvNet is able to recognize scenes and the system is able to suggest relevant tags such as'bridge', 'railway' and'tennis' while Figure 2 shows an example of ConvNets being used for recognizing everyday objects, humans and animals. Lately, ConvNets have been effective in several Natural Language Processing tasks (such as sentence classification) as well. ConvNets, therefore, are an important tool for most machine learning practitioners today. However, understanding ConvNets and learning to use them for the first time can sometimes be an intimidating experience. The primary purpose of this blog post is to develop an understanding of how Convolutional Neural Networks work on images. If you are new to neural networks in general, I would recommend reading this short tutorial on Multi Layer Perceptrons to get an idea about how they work, before proceeding. Multi Layer Perceptrons are referred to as "Fully Connected Layers" in this post.