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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/s

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/linear-sensor.html 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.

chestervonwinch 9y 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.
Florin_Andrei 9y 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".
- kmmlng 9y 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.
haddr 9y 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.

chestervonwinch 9y 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.

Florin_Andrei 9y 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".

kmmlng 9y 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.

haddr 9y 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.