I'm really interested in training NNs in the frequency domain:
- a ton of data (JPEGs, MPEGs, MFCCs) exists in the compressed form of FFT-ed features
- FFTs are hardware encoded/decoded & optimised
- FFTs are great at separating signal from noise, leading to a huge reduction in input features (eg JPEG encoded) as compared to raw pixel values
- convolutions in the time domain are just multiplications in the frequency domain
- converting to log polar coordinates turns them into additions
- distance between two log polar coordinates could be the loss function
However, I've not had much luck getting a frequency domain network like this to converge. Anyone tried something similar with success?
- a ton of data (JPEGs, MPEGs, MFCCs) exists in the compressed form of FFT-ed features
- FFTs are hardware encoded/decoded & optimised
- FFTs are great at separating signal from noise, leading to a huge reduction in input features (eg JPEG encoded) as compared to raw pixel values
- convolutions in the time domain are just multiplications in the frequency domain
- converting to log polar coordinates turns them into additions
- distance between two log polar coordinates could be the loss function
However, I've not had much luck getting a frequency domain network like this to converge. Anyone tried something similar with success?