Receptive field: Difference between revisions
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== Introduction == | == Introduction == | ||
Receptive fields used in an artificial neural networks are similar to the biological explanation. They are a sensory neuron where a prompt, or stimulation, changes the action of the neuron.<ref> https://en.wikipedia.org/wiki/Receptive_field </ref> In other words, the fields gather information to be used by the neurons. They are used in relation to [https://en.wikipedia.org/wiki/Convolutional_neural_network convolutional neural networks (CNNs)]. The neurons are arranged in a [https://en.wikipedia.org/wiki/File:Conv_layers.png 3-D array]. <ref> https://en.wikipedia.org/wiki/Receptive_field#In_the_context_of_neural_networks </ref> | |||
== How does it work or a deeper look == | == How does it work or a deeper look == | ||
The receptive field gathers information and sends the information to the neurons in the CNNs. Rather than a more traditional neural network where every input neuron is linked to every other neuron at the first level, Receptive fields allow for "pre-processing" of just a certain grid (or part) of the input image. | |||
== Examples == | == Examples == | ||
The Gaussian receptive fields are being used in test on cars to allow them to be detected. <ref> http://www.inase.org/library/2015/books/bypaper/MCSI/MCSI-42.pdf </ref> | |||
== Pictures, diagrams == | == Pictures, diagrams == | ||
# [https://en.wikipedia.org/wiki/File:Conv_layers.png An image showing the arrangement of neurons in the CNN.] | |||
# [https://upload.wikimedia.org/wikipedia/commons/thumb/8/8a/Conv_layers.png/184px-Conv_layers.png An image showing the receptive field and the CNN.] | |||
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== External links == | == External links == | ||
* | * http://www.inase.org/library/2015/books/bypaper/MCSI/MCSI-42.pdf | ||
* | * https://en.wikipedia.org/wiki/Receptive_field | ||
* | * https://en.wikipedia.org/wiki/Receptive_field#In_the_context_of_neural_networks | ||
* | * https://en.wikipedia.org/wiki/Convolutional_neural_network | ||
== References == | == References == | ||
Latest revision as of 12:56, 14 March 2018
This is student work which has not yet been approved as correct by the instructor
Case study notes[1]
Introduction[edit]
Receptive fields used in an artificial neural networks are similar to the biological explanation. They are a sensory neuron where a prompt, or stimulation, changes the action of the neuron.[2] In other words, the fields gather information to be used by the neurons. They are used in relation to convolutional neural networks (CNNs). The neurons are arranged in a 3-D array. [3]
How does it work or a deeper look[edit]
The receptive field gathers information and sends the information to the neurons in the CNNs. Rather than a more traditional neural network where every input neuron is linked to every other neuron at the first level, Receptive fields allow for "pre-processing" of just a certain grid (or part) of the input image.
Examples[edit]
The Gaussian receptive fields are being used in test on cars to allow them to be detected. [4]
Pictures, diagrams[edit]
- An image showing the arrangement of neurons in the CNN.
- An image showing the receptive field and the CNN.
External links[edit]
- http://www.inase.org/library/2015/books/bypaper/MCSI/MCSI-42.pdf
- https://en.wikipedia.org/wiki/Receptive_field
- https://en.wikipedia.org/wiki/Receptive_field#In_the_context_of_neural_networks
- https://en.wikipedia.org/wiki/Convolutional_neural_network