Convolutional Neural Network / Ars Electronica Futurelab
This Convolutional Neural Network (CNN) learned through many training samples to recognize certain objects. The network consists of several consecutive layers that have learned during the training phase to recognize different characteristics in an image and pass on this information in turn to the next layer. While the first layers recognize more primitive characteristics such as straight lines, colors, and curves, the next layers specialize in more complex forms. The network VGG16 used here is one of the best-known models of this kind.
Photo: Philipp Greindl
Convolutional Neural Network / Ars Electronica Futurelab
This Convolutional Neural Network (CNN) learned through many training samples to recognize certain objects. The network consists of several consecutive layers that have learned during the training phase to recognize different characteristics in an image and pass on this information in turn to the next layer. While the first layers recognize more primitive characteristics such as straight lines, colors, and curves, the next layers specialize in more complex forms. The network VGG16 used here is one of the best-known models of this kind.
Photo: Philipp Greindl