WaLLoC couples an invertible wavelet packet transform with a linear-projection encoder and nonlinear decoder to produce low-dimensional, quantization-resilient codes for compressed-domain learning.
WaLLoC uses the computationally cheap and invertible wavelet packet transform [14] to expose signal redundancies prior to autoencoding
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Learned Compression for Compressed Learning
WaLLoC couples an invertible wavelet packet transform with a linear-projection encoder and nonlinear decoder to produce low-dimensional, quantization-resilient codes for compressed-domain learning.