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by sigstoat 3194 days ago
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.
3 comments
Florin_Andrei 3194 days ago
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 it would still be the same matrix, just represented differently. I don't think the CNN cares either way.

I would go as far as saying that the 1D-ness of the input is just "in your head".

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kmmlng 3193 days ago
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.

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chestervonwinch 3194 days ago
1D signals are typically associated with time series data. There's a ton of work on audio signals, e.g., speech recognition.

edit: I'm not sure if you're asking specifically for examples of CNNs applied to linear image sensor data, or if you're asking whether CNNs have been applied to any 1D input data.

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haddr 3193 days ago
same like 2D, but the convolution layer is a vector. It's similar like the case of CNN applied to NLP, in this case the text is also 1D on input.
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