Talk:Convolutional neural network
Add topic| This is the talk page for discussing improvements to the Convolutional neural network article. This is not a forum for general discussion of the subject of the article. |
Article policies
|
| Find sources: Google (books · news · scholar · free images · WP refs) · FENS · JSTOR · TWL |
| Archives: 1Auto-archiving period: 3 months |
A fact from this article appeared on Wikipedia's Main Page in the "Did you know?" column on December 9, 2013. The text of the entry was: Did you know ... that convolutional neural networks have achieved performance double that of humans on some image recognition problems? |
| This article is rated C-class on Wikipedia's content assessment scale. It is of interest to the following WikiProjects: | ||||||||||||||||||||||||||||||||||||||||||||||||||||
| ||||||||||||||||||||||||||||||||||||||||||||||||||||
Introduction
[edit]"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". 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? Iuvalclejan (talk) 22:47, 25 January 2025 (UTC)
- I made the change. 2600:6C5D:577F:F44E:B9B2:E830:3647:8315 (talk) 14:20, 27 January 2025 (UTC)
Wiki Education assignment: Linguistics in the Digital Age
[edit]
This article was the subject of a Wiki Education Foundation–supported course assignment, between 15 January 2025 and 9 May 2025. Further details are available on the course page. Student editor(s): AshlaMaOmao (article contribs).
— Assignment last updated by FblthpTheLost (talk) 00:10, 8 May 2025 (UTC)
Should deconvolution use matrix inverses rather than transposes?
[edit]If you are doing true de-convolution, you need to multiply by the inverse of the convolution operator. Only in special cases is that the same as the transpose. Unless the argument that this isn't real convolution makes a difference, I assume that the same applies here. If it really is a special case, that needs highlighting. Actually, in real convolution, there is no true inverse, as information is lost.
- Wikipedia Did you know articles
- C-Class Computing articles
- Mid-importance Computing articles
- All Computing articles
- C-Class Computer science articles
- Mid-importance Computer science articles
- WikiProject Computer science articles
- C-Class Artificial Intelligence articles
- Mid-importance Artificial Intelligence articles
- WikiProject Artificial Intelligence articles