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shahaff32 OP t1_is0ths5 wrote

Reply to comment by londons_explorer in [R] Wavelet Feature Maps Compression for Image-to-Image CNNs by shahaff32

This is aimed mostly at edge devices, where an accelerator is not available (e.g. mobile phones), or you want to design a cheaper chip for a product that requires running such networks (e.g. autonomous vehicles)

This work was, in fact, partially supported by AVATAR consortium, aimed at smart vehicles. https://avatar.org.il/

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londons_explorer t1_is11x8p wrote

Sure this work was aimed at that, but these same techniques can be used to make a datacenter-scale inference machine into an even more powerful one.

And presumably if a way can be found to do backpropagation in 'wavelet domain', then training could be done like this too.

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shahaff32 OP t1_is13c2c wrote

We are in fact doing the backpropagation in the wavelet domain :)

The gradient simply goes through the inverse wavelet transform

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See WCC/util/wavelet.py in our GitHub repo, lines 52-83 define the forward/backward of WT and IWT.

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NeverCast t1_is2f1jt wrote

The immediate use case for me was on autonomous flight vehicles where weight and battery usage matters

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