Gradient $\ell_1$ Regularization for Quantization Robustness

ICLR 2020 Milad AlizadehArash BehboodiMart van BaalenChristos LouizosTijmen BlankevoortMax Welling

We analyze the effect of quantizing weights and activations of neural networks on their loss and derive a simple regularization scheme that improves robustness against post-training quantization. By training quantization-ready networks, our approach enables storing a single set of weights that can be quantized on-demand to different bit-widths as energy and memory requirements of the application change... (read more)

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