# Week 4 Neural style transfer

**URL:** <https://community.deeplearning.ai/t/week-4-neural-style-transfer/289460>\
**Category:** Convolutional Neural Networks\
**Tags:** coursera-platform\
**Created:** [March 13, 2023, 5:40am UTC](https://community.deeplearning.ai/t/week-4-neural-style-transfer/289460 "2023-03-13T05:40:34Z")\
**Posts on this page:** 1\
**Showing post:** 8

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**Author:** ![rmwkwok](https://sea1.discourse-cdn.com/dlai/user_avatar/community.deeplearning.ai/rmwkwok/32/126977_2.png) [@rmwkwok](https://community.deeplearning.ai/u/rmwkwok)\
**Post date:** [November 3, 2023, 2:23pm UTC](https://community.deeplearning.ai/t/week-4-neural-style-transfer/289460/8 "2023-11-03T14:23:43Z")

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Hi @James_Siddle,

I personally like to think of one filter (f x f x input\_number\_of\_channels) as one neuron, because if I have 23 neurons in this Dense layer, then the layer will produce 23 features; similarly, if I have 23 filters in this Conv layer, then the layer will produce 23 feature maps.

So, Dense → Conv; neurons → filters; features → feature maps.

I know it is not the norm that people actually call a filter as a neuron, but I like to think of it that way.

> [@James\_Siddle](#):
>
> What’s unclear to me how that intuition might translate into the later layers in terms of the visualisation process shown. I suspect that there is some clever “unwinding” going on to recover the image patches that are shown for the later layers, because the filters for those layers will be detecting complex objects based on features detected in earlier layers rather than arrangements of pixels.

Yes, and our mentor @hackyon has shared a paper [in this post](https://community.deeplearning.ai/t/a-doubt-in-visualizing-deep-network/477057/12), which I think you may want to have a look.

Thanks for your amazing analysis.

Cheers,  
Raymond

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