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I have a background in classic image processing and machine vision and back in the olden days we had the opposite problem: algorithms were just too specific to

I have a background in classic image processing and machine vision and back in the olden days we had the opposite problem: algorithms were just too specific to build useful applications. It's easy to detect lines and circles with a Hough Transform or do template matching for features that very closely match a sample. However, working up the chain it never came together, detecting cars in a parking lot, a relatively simple task with CNNs, was a very difficult problem. I wonder if enough work is being done to combine the achievements of each field. Whenever I see adversarial examples I wonder why people aren't doing more preprocessing to root out obvious problems with normalization in scale, color, perspective, etc. Also, if we could feed networks with higher level descriptors instead of feeding low-information-density color images, wouldn't that make life easier. I'm sure I'm not the only one thinking this, is there any good research being done in that space?

TonyTrapp 5y ago
I'm often thinking the same, and am only aware of a single popular library doing that: rnnoise [0] This library combines "classic" digital signal processing with a smaller RNN. As a result, it's smaller, faster and probably also has less uncanny edge cases than approaches that use an RNN for the complete processing chain. I think many use cases could benefit from this approach. [0] https://jmvalin.ca/demo/rnnoise/ https://jmvalin.ca/demo/rnnoise/
gradys 5y ago
A library to do this that integrated well with typical inference and and training systems would be a great contribution!
mjburgess 5y ago
Because the new wave don't know how to do this. Deep Learning as AI was sold as "statistics in place of physics" -- ie., no need to understand reality (the target domain), simply brute force it. The peddlers of such a message were bamboozled by early successes and lacked sufficient experience of empirical science to realise this was never going to work. No NN will discover the universal law of gravitation from any dataset not collected on the basis of knowing this universal law. With 'statistics as theory' there can never be new theory, as a new theory is a precondition of a new dataset.
nestorD 5y ago
My feeling is that: - lots of people in the DNN for machine vision community do not have a background in classical techniques. - a lot of classical techniques and preprocessing pass make no real difference when applied to the input of a DNN and are thus worth eliminating from the pipeline to simplify it (this has been my experience). However, I do think that there are gain to be gotten by combining classical image processing ideas with neural networks. It just hasn't really happened yet.
omeze 5y ago
There's some stuff happening, eg applying anti-aliasing to improve shift invariance: https://richzhang.github.io/antialiased-cnns/ https://richzhang.github.io/antialiased-cnns/ (also check out the related papers)

TonyTrapp 5y ago

I'm often thinking the same, and am only aware of a single popular library doing that: rnnoise [0] This library combines "classic" digital signal processing with a smaller RNN. As a result, it's smaller, faster and probably also has less uncanny edge cases than approaches that use an RNN for the complete processing chain. I think many use cases could benefit from this approach. [0] https://jmvalin.ca/demo/rnnoise/ https://jmvalin.ca/demo/rnnoise/

gradys 5y ago

A library to do this that integrated well with typical inference and and training systems would be a great contribution!

mjburgess 5y ago

Because the new wave don't know how to do this. Deep Learning as AI was sold as "statistics in place of physics" -- ie., no need to understand reality (the target domain), simply brute force it. The peddlers of such a message were bamboozled by early successes and lacked sufficient experience of empirical science to realise this was never going to work. No NN will discover the universal law of gravitation from any dataset not collected on the basis of knowing this universal law. With 'statistics as theory' there can never be new theory, as a new theory is a precondition of a new dataset.

nestorD 5y ago

My feeling is that: - lots of people in the DNN for machine vision community do not have a background in classical techniques. - a lot of classical techniques and preprocessing pass make no real difference when applied to the input of a DNN and are thus worth eliminating from the pipeline to simplify it (this has been my experience). However, I do think that there are gain to be gotten by combining classical image processing ideas with neural networks. It just hasn't really happened yet.

omeze 5y ago

There's some stuff happening, eg applying anti-aliasing to improve shift invariance: https://richzhang.github.io/antialiased-cnns/ https://richzhang.github.io/antialiased-cnns/ (also check out the related papers)