Fast Trainable Multilinear Bases for Image Compression
The episode reviews the paper "Fast Trainable Multilinear Bases for Image Compression," which makes classical transforms like FFT and DCT trainable while maintaining speed and exact invertibility. The hosts discuss how this approach, using unitary gates and Riemannian optimization, achieves gains in compression for sketches and natural photos. Limitations include needing offline training per dataset.