The success of Artificial Intelligence in the past few years can be directly attributed to deep neural network. From image recognition to smart thermostats and self-driving cars, AI has access to everything including your smartphones. That’s how universal neural nets have become. However, there is a growing concern that some of the vital principles of this system may not overcome the major problems faced by AI. This means “traditional” neural network may leave the market and possibly be replaced by something cutting-edgier i.e. “capsule” neural nets.
What is Traditional/Artificial Neural Network and How This Works?
Back in school days, you must have examined the simulation of single biological neuron of a brain. It explains the process of flow of information from various direction, which gets accumulated and processed in neuron and from there, results flow out. This process gives neuron the ability to react based on previously learned pattern. Science has replicated this process by creating a structure that processes information like a biological neuron. Instead, the technological neural network is mathematical based, where the information passes through artificial neuron and from there results flow out. This process has now become a mathematical formula and is used for solving simple problems.
As for the brain, artificial neuron network powers are connecting sets of networks together in layers. When you connect them in layers, the mathematical formula becomes something like a multi-dimensional polynomial. This allows complex problems like 3d solution surface, to be discovered, solved and used for our benefit. As before, information flows in and result flows out but this time input flowing through the second layer is the output of the first layer. This exact step for single layer is simply repeated for each layer of neural network. This is how an artificial neural network works.
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What is Capsule Network and How Do They Work?
This is a new kind of neural network comprised of capsules. Here, small clusters of each neurons are connected to a particular part. To understand this concept, imagine a picture comprised of different components that are connected to their respective neuron. Each neuron’s activity determines the characteristics of the component of the image and each capsule is accountable for recognizing a single component.
Capsule Networks are a network of groups of neurons, where each neuron represents an independent identifiable entity of an image. Capsule Network is constructed to overcome the inadequacies of Convolutional Neural Networks (CNNs). For instance, Capsule Networks specify orientation, localization and precise location of each component within an image, which is absent in Traditional Neural Network.
Application of Capsule Network
The main limitation of Traditional Neural Network is computer vision. Capsule Network aims to solve this problem, in fact, the whole idea of developing Capsule Network is to solve this problem.
Traditional Neural Networks are currently facing problems in recognizing images with lesser data or detecting the contextual contingency between components within images. Capsule Network can be a problem-solver here.
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Traditional Neural Network goes through huge quantities of data sources to learn while Capsule Network adjustment is different.
While some proclaim that Capsule, Network promises the departure of Traditional Neural Network, numerous researchers have pointed out ranges of application where these networks might not perform so well. Till then, we can just wait and watch. This is all about Neural networks, if you have anything to share, please comment in the section below.