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2020 25th International Conference on Pattern Recognition (ICPR) by Ahmetoglu, Alper (author);Alpaydin, Ethem (author) is a nonfiction available to read on EtoBox.

What is 2020 25th International Conference on Pattern Recognition (ICPR) about?

Generative adversarial networks (GANs) are deep neural networks that allow us to sample from an arbitrary probability distribution without explicitly estimating the distribution. There is a generator that takes a latent vector as input and transforms it into a valid sample from the distribution. There is also a discriminator that is trained to discriminate such fake samples from true samples of the distribution; at the same time, the generator is trained to generate fakes that the discriminator cannot tell apart from the true samples. Instead of learning a global generator, a recent approach involves training multiple generators each responsible from one part of the distribution. In this work, we review such approaches and propose the hierarchical mixture of generators, inspired from the hierarchical mixture of experts model, that learns a tree structure implementing a hierarchical clustering with soft splits in the decision nodes and local generators in the leaves. Since the generators are combined softly, the whole model is continuous and can be trained using gradient-based optimization, just like the original GAN model. Our experiments on five image data sets, namely, MNIST, Fas

Who reads 2020 25th International Conference on Pattern Recognition (ICPR)?

Author

Ahmetoglu, Alper (author);Alpaydin, Ethem (author)

Publisher

IEEE

Published

2021

Language

EN

Category

nonfiction