This image was uploaded in the JPEG format even though it consists of non-photographic data. This information could be stored more efficiently or accurately in the PNG or SVG format. If possible, please upload a PNG or SVG version of this image without compression artifacts, derived from a non-JPEG source (or with existing artifacts removed). After doing so, please tag the JPEG version with {{Superseded|NewImage.ext}} and remove this tag. This tag should not be applied to photographs or scans. If this image is a diagram or other image suitable for vectorisation, please tag this image with {{Convert to SVG}} instead of {{BadJPEG}}. If not suitable for vectorisation, use {{Convert to PNG}}. For more information, see {{BadJPEG}}.
Summary
DescriptionNeural Abstraction Pyramid.jpg
English: Sketch of the information flow in the Neural Abstraction Pyramid.
This neural architecture for image interpretation was inspired by the visual cortex.
Unlike feed-forward models such as LeNet, information flows also laterally between neighboring elements on the same level of abstraction and in a top-down direction from higher-layer representations to lower layers. This constitutes a hierarchical, recurrent, convolutional neural network that can iteratively resolve local ambiguities and recursively process video.
to share – to copy, distribute and transmit the work
to remix – to adapt the work
Under the following conditions:
attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
share alike – If you remix, transform, or build upon the material, you must distribute your contributions under the same or compatible license as the original.