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Found 3 papers, 2 papers with code
NeRN -- Learning Neural Representations for Neural Networks
Neural Representations have recently been shown to effectively reconstruct a wide range of signals from 3D meshes and shapes to images and videos.
Wavelet Feature Maps Compression for Image-to-Image CNNs
Convolutional Neural Networks (CNNs) are known for requiring extensive computational resources, and quantization is among the best and most common methods for compressing them.
Wavelet Feature Maps Compression for Low Bandwidth Convolutional Neural Networks
In this paper, we propose Wavelet Compressed Convolution (WCC)---a novel approach for activation maps compression for $1\times1$ convolutions (the workhorse of modern CNNs).
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