Table of Contents
What is deconvolutional neural network?
Deconvolutional networks are convolutional neural networks (CNN) that work in a reversed process. A convolutional neural network emulates the workings of a biological brain’s frontal lobe function in image processing. A deconvolutional neural network constructs upwards from processed data.
What is an adversarial neural network?
A generative adversarial network (GAN) is a machine learning (ML) model in which two neural networks compete with each other to become more accurate in their predictions. GANs typically run unsupervised and use a cooperative zero-sum game framework to learn. Essentially, GANs create their own training data.
How does Deconvolutional layer work?
A deconvolution is a mathematical operation that reverses the effect of convolution. Imagine throwing an input through a convolutional layer, and collecting the output. Now throw the output through the deconvolutional layer, and you get back the exact same input.
What means Deconvolutional?
Definition of deconvolution : simplification of a complex signal (as instrumental data) usually by removal of instrument noise.
How does a GAN generator work?
In its most basic form, a GAN takes random noise as its input. The generator then transforms this noise into a meaningful output. By introducing noise, we can get the GAN to produce a wide variety of data, sampling from different places in the target distribution.
Is deepFake GAN?
A deepFake video created by a Generative Adversarial Network or GAN. GANs can be used for a number of exciting things but what has caught the public’s imagination is the use of GANs to create deepFakes, i.e. to create videos of talking people where the face has been swapped for some else.
What is the purpose of semantic segmentation?
Semantic Segmentation is a technique that enables us to differentiate different objects in an image. It can be considered an image classification task at a pixel level.
Why do we need semantic segmentation?
Semantic Segmentation is used to identify salient elements in medical scans. It is especially useful to identify abnormalities such as tumors. The accuracy and low recall of algorithms are of high importance for these applications.