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.
WaL- LoC’s encoder projects high-dimensional signal patches to low-dimensional latent representations, providing a reduction of up to 20 ×
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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.