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The article states that it could be a way to use advances in computer vision to succeed where RNNs have difficulties. (Kinda sceptical about the success of thi
by tvirosi 5y ago
The article states that it could be a way to use advances in
computer vision to succeed where RNNs have difficulties. (Kinda sceptical about the success of this technique but that's the claim.)
- cfcf14 5y agoThis is a relatively common trick in ML - mapping temporal data to image form so one can use the relatively efficient convolutional operations on it. An example would be mapping audio data via a short-time fast fourier transform (https://en.wikipedia.org/wiki/Short-time_Fourier_transform https://en.wikipedia.org/wiki/Short-time_Fourier_transform) and using 2d convolutions on that for processing. Architectural improvements in 1D convolutions (atrous convolutions) have possibly surpassed these techniques in some areas though, I'm not sure.