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An Intuitive Explanation of Convolutional Neural Networks (2016)

ujjwalkarn.me

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Re: An Intuitive Explanation of Convolutional Neural Networks (2016)

#21

anyone happen to be familiar with any uses of CNNs on 1D "images"? (like you'd get from linear image sensors https://toshiba.semicon-storage.com/ap-en/product/sensor/lin... ) i hit up google scholar occasionally looking for references, but literally everything seems to be applying them to 2D images.

Well, what happens if you build a 1D-input CNN in TensorFlow and train it the usual way? Does it work? Seems like it should. What's even the difference between 1D inputs and 2D inputs? It's all a bunch of numbers anyway. I don't think it really matters if the pixels are arranged (as you see them) in a neat rectangle vs in a straight line. You could take a 2D matrix and enumerate it as a linear string of numbers and i…

I would argue that in a signal (1D) you can expect some sort of relationship between consecutive elements. In an image (in essence a 2D signal), you can expect a relationship between consecutive elements not just on the horizontal, but also on the vertical axis.

If you arbitrarily represent a signal as a 2D matrix, then abrupt changes in the gradient on the vertical axis are meaningless. But the same is not true in an image, which is naturally represented as a 2D matrix. Here, a sudden change on the vertical axis usually corresponds to an edge in the image.

If you represent an image as a 1D array, you throw away spatial information. So I'm not sure about the 1D-ness just being in ones head.

Re: An Intuitive Explanation of Convolutional Neural Networks (2016)

#22
post #19

Earlier quoted context omitted.

Can you explain the difference between the two? I'm new to CNNs and have been wondering this myself - this SO answer says that they're the same thing: https://stats.stackexchange.com/questions/154798/difference-...

Sorry, edited a bit for clarity after thinking some more about it. The kernel of a filter would be it's impulse response, which is what you convolve by to get the filter response. That's where the sloppy terminology comes from. A kernel though does not need to be a filter. A kernel is a function whose product maps a point in one domain onto another domain. For example, the Fourier transform has a kernel of e^jwt. The…

Personally I think you're being a bit pedantic and fuzzy yourself on the terminology. For the purposes of CNN, it's perfectly fine to think of them as the same thing and the kernel in this case is simply not the same as the "kernel" in linear algebra you alluded to. In fact, it's so different, I don't even know why you'd bother to mention it.

Re: An Intuitive Explanation of Convolutional Neural Networks (2016)

#23

I actually don't think this is a good explanation at all. I'm not saying it's badly written, just that it's not a good explanation for the stated purpose (serving as an intuitive explanation). To this point, the article is certainly NOT intuitive if you don't already understand image convolution. The explanation is also very long and rambling. While I understand the author has made an effort, I don't think the articl…

It's immensely clear...

Re: An Intuitive Explanation of Convolutional Neural Networks (2016)

#24

I actually don't think this is a good explanation at all. I'm not saying it's badly written, just that it's not a good explanation for the stated purpose (serving as an intuitive explanation). To this point, the article is certainly NOT intuitive if you don't already understand image convolution. The explanation is also very long and rambling. While I understand the author has made an effort, I don't think the articl…

man i came back after 2 days just to comment on this: you're high. the article is crystal clear and intuitive. he covers each layer's design, purpose, and effect in intuitive terms.

you on the other hand haven't said literally anything except vaguely criticized. look i'll show you how it's done:

>The explanation is also very long and rambling. While I understand the author has made an effort, I don't think the article really presents the subject matter in a new way: I can learn all of this elsewhere. This is a common problem when people write about complex subject matter without fully understanding the knowledge gap between teacher and audience.

these two sentences have nothing to do with each other: that the explanation isn't novel has nothing to do with elided gaps between expositors and readers (wherein usually the exposition is too complex, not too simple as you've confused it).

>If I were the author, I might try to read up on technical communication and spend some time figuring out how to correctly simply something.

vague. read from where? which chapters? simplify which parts?

>I do like the animation, but again, it only serves to show how image convolution works, and doesn't actually teach us anything about a CNN.

it's like you think that one animation should explain the entire CNN. did you actually read the post? that image explain convolutions and is the absolute standard explanation for convolving with a filter/kernel.

>I would suggest the author break the document into three separate sections

better in that at least it's concrete advice. i suggest you include more points like this.

>images are just matrices

are you suggesting the author goes into CCDs? ADCs? now that would be a rambling post.

>That way you counteract the information blindness that occurs from simplification by providing the information later.

that's terrible advice. detail should be evenly distributed through the article. look at any journal article: except for the appendices all of the meat is in the body not in the conclusion.

>Otherwise, this article is really more of a data dump than an intuitive explanation,

a data dump would be just code. this is in fact an intuitive explanation that uses the classification of dogs/cats/boats/bird as the framework, so there's a structure, terms are defined, there's context (lenet etc.), and there are references.

>and since it doesn't really teach us anything we can't learn elsewhere, I don't see what it contributes.

blog articles don't need to be novel.

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