Shallow Art: Art Extension Through Simple Machine Learning

10/21/2019
by   Kyle Robinson, et al.
0

Shallow Art presents, implements, and tests the use of simple single-output classification and regression models for the purpose of art generation. Various machine learning algorithms are trained on collections of computer generated images, artworks from Vincent van Gogh, and artworks from Rembrandt van Rijn. These models are then provided half of an image and asked to complete the missing side. The resulting images are displayed, and we explore implications for computational creativity.

READ FULL TEXT

page 2

page 4

research
12/13/2021

A Case For Noisy Shallow Gate-Based Circuits In Quantum Machine Learning

There is increasing interest in the development of gate-based quantum ci...
research
05/19/2018

Reconciled Polynomial Machine: A Unified Representation of Shallow and Deep Learning Models

In this paper, we aim at introducing a new machine learning model, namel...
research
03/16/2023

Machine learning based biomedical image processing for echocardiographic images

The popularity of Artificial intelligence and machine learning have prom...
research
01/06/2020

Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge

Purpose: To advance research in the field of machine learning for MR ima...
research
03/27/2018

Classification of crystallization outcomes using deep convolutional neural networks

The Machine Recognition of Crystallization Outcomes (MARCO) initiative h...
research
01/17/2020

Generación automática de frases literarias en español

In this work we present a state of the art in the area of Computational ...

Please sign up or login with your details

Forgot password? Click here to reset