Towards deep neural network compression via learnable wavelet transforms
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Wavelets are well known for data compression, yet have rarely been applied to the compression of neural networks. In this paper, we show how the fast wavelet transform can be applied to compress linear layers in neural networks. Linear layers still occupy a significant portion of the parameters in recurrent neural networks (RNNs). Through our method, we can learn both the wavelet bases as well as corresponding coefficients to efficiently represent the linear layers of RNNs. Our wavelet compressed RNNs have significantly fewer parameters yet still perform competitively with state-of-the-art on both synthetic and real-world RNN benchmarks. Wavelet optimization adds basis flexibility, without large numbers of extra weights
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