Wavelet optimization for Network compression

Wavelets are uncommon in machine learning, systems with learnable wavelets, in particular, are rare. Promising applications of wavelets in neural networks exist. Adaptive wavelets for network compression are explored in the new paper ‘Neural network compression via learnable wavelet transforms‘. By defining new wavelet loss terms based on the product filter approach to wavelet design, the wavelets become part of the network architecture. They can be learned just like any other weights. Source code implementing wavelet optimization in PyTorch is available on Github.

Jaxlets – Fast Wavelet Transformations in JAX

The fast wavelet transform is an important signal processing algorithm. Jet a differentiable implementation in JAX has been missing so far, I have therefore opened my implementation . It supports the one and two dimensional analysis and synthesis transforms. As well as an implementation of the forward wavelet packet transform. The plot below shows an analysis of a linear chirp signal using a Daubechies wavelet.

Wavelet analysis of a linear chirp signal.

As the chirps’ frequency increases we see that the wavelet coefficients rise as well.

Source code is available at https://github.com/v0lta/jaxlets .

Video Prediction à la Fourier

Video frame prediction is a very challenging problem. Many recent neural network based solution-attempts trained using a mean squared error lead to blurry predictions. My most recent paper currently under review proposes to use Phase correlation and the Fourier-Shift theorem estimate changes and transform current images into predictions. A demo is shown below. The video shows ground truth (left), shift prediction (middle) and an off the shelf GRU prediction (right).

Source code is available on github .

A more detailed description is available in the paper .

Spectral-RNN

Fourier methods have a long and proven track record as an excellent tool in data processing. Integrating Fourier methods into complex recurrent neural network architectures is therefore an important goal. I integrated the short-time Fourier transform into a recurrent (complex-valued) network structure. This helps when dealing with hard prediction tasks such as human motion prediction, a demo paper and code are available.

Paper: https://arxiv.org/pdf/1812.05645.pdf

Code: https://github.com/v0lta/Spectral-RNN