Talk:Convolutional neural network: Difference between revisions

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| dykentry = ... that '''[[Convolutional neural network|convolutional neural networks]]''' have achieved performance double that of humans on some image recognition problems?

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== Feature Maps ==

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Need to introduce what feature maps are for nontechnical readers. <!-- Template:Unsigned --><small class="autosigned">—&nbsp;Preceding [[Wikipedia:Signatures|unsigned]] comment added by [[User:Shsh16|Shsh16]] ([[User talk:Shsh16#top|talk]] • [[Special:Contributions/Shsh16|contribs]]) 18:24, 15 February 2017 (UTC)</small> <!--Autosigned by SineBot-->

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== Non-linear Pooling ==

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It says in the article: "Another important concept of CNNs is pooling, which is a form of '''non-linear''' down-sampling."

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I don't think this is correct. There are pooling techniques, like average pooling which is mentioned in this same section, which are forms of linear down-sampling. I would remove the "non-linear." [[Special:Contributions/194.117.26.63|194.117.26.63]] ([[User talk:194.117.26.63|talk]]) 15:06, 13 May 2016 (UTC)

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== Plagiarism in "Layer patterns" ==

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The text seems is copied from https://cs231n.github.io/convolutional-networks/#layerpat without any attribution <small><span class="autosigned">—&nbsp;Preceding [[Wikipedia:Signatures|unsigned]] comment added by [[User:Jkoab|Jkoab]] ([[User talk:Jkoab|talk]] • [[Special:Contributions/Jkoab|contribs]]) 01:41, 8 June 2016 (UTC)</span></small><!-- Template:Unsigned --> <!--Autosigned by SineBot-->

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:Indeed. Deleted copyvio text, see below. [[User:Maproom|Maproom]] ([[User talk:Maproom|talk]]) 09:55, 8 June 2016 (UTC)

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== Suggestion: Move the section "Regularization methods" to a new page ==

The methods listed here are applicable to deep learning in general.

This topic should be moved into a new page. [[User:OhadRubin|OhadRubin]] ([[User talk:OhadRubin|talk]]) 06:38, 27 November 2018 (UTC)

== Parameter Sharing Clarifications ==

In the "Parameter sharing" section, "relax the parameter sharing scheme" is written, but what this actually means is unclear. <!-- Template:Unsigned --><small class="autosigned">—&nbsp;Preceding [[Wikipedia:Signatures|unsigned]] comment added by [[User:Ephsc|Ephsc]] ([[User talk:Ephsc#top|talk]] • [[Special:Contributions/Ephsc|contribs]]) 16:22, 27 September 2019 (UTC)</small> <!--Autosigned by SineBot-->

== What is convolutional about a convolutional neural network? ==

The article fails to explain what the connection between CNNs and [[convolution | convolutions]] are in any meaningful way. In particular, convolutions don't act on vectors; they act on functions. Comparing with the equation on the page for convolutions, there's obviously something analogous. --[[User:Stellaathena|Stellaathena]] ([[User talk:Stellaathena|talk]]) 16:51, 14 December 2020 (UTC)

its actually the dsp version of a cross correlation, not a convolution. its a misnomer to call it convolution.-AS

== Inaccurate information about Convolutional layers ==

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I would also suggest merging the section "Definition" into the introduction. The definition section is only two sentences and it feels it would be better placed at the introduction.

== Introduction ==

"only 25 neurons are required to process 5x5-sized tiles". Shouldn't that be "weights" and not "neurons"? Earlier it said "10,000 weights would be required for processing an image sized 100 × 100 pixels". [[User:Ulatekh|Ulatekh]] ([[User talk:Ulatekh|talk]]) 15:53, 19 March 2024 (UTC)

:Absolutely, you're right. I was going to ask the same question. 25 weights for each neuron in the second layer from each neuron in the input layer, and all these 25 weights don't vary as the filter is slid across the input. Do you want to make the correction or should I, since the original editor is not responding? [[User:Iuvalclejan|Iuvalclejan]] ([[User talk:Iuvalclejan|talk]]) 22:47, 25 January 2025 (UTC)

::I made the change. [[Special:Contributions/2600:6C5D:577F:F44E:B9B2:E830:3647:8315|2600:6C5D:577F:F44E:B9B2:E830:3647:8315]] ([[User talk:2600:6C5D:577F:F44E:B9B2:E830:3647:8315|talk]]) 14:20, 27 January 2025 (UTC)

== Big picture ==

Why are convolutional NNs (or networks with several Convolutional layers as opposed to none) more useful especially for images, than networks with only fully connected layers? You mention something about translational equivariance in artificial NNs and in the visual cortex in brains, but this is a property of the neural network, not of its inputs. It's a way to reduce the number of weights per layer, but why isn't it universally useful (for all inputs and all output tasks), and why is it better for images than other ways of reducing the number of weights per layer? [[User:Iuvalclejan|Iuvalclejan]] ([[User talk:Iuvalclejan|talk]]) 23:50, 25 January 2025 (UTC)

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