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Model parameter regularization is a widely used technique to improvegeneralization, but also can be used to shape the weight distributions forvarious purposes. In this work, we shed light on how weight regularization canassist model quantization and compression techniques, and then propose rangeregularization (R^2) to further boost the quality of model optimization byfocusing on the outlier prevention. By effectively regulating the minimum andmaximum weight values from a distribution, we mold the overall distributioninto a tight shape so that model compression and quantization techniques canbetter utilize their limited numeric representation powers. We introduce L-infregularization, its extension margin regularization and a new soft-min-maxregularization to be used as a regularization loss during full-precision modeltraining. Coupled with state-of-the-art quantization and compressiontechniques, models trained with R^2 perform better on an average, specificallyat lower bit weights with 16x compression ratio. We also demonstrate that R^2helps parameter constrained models like MobileNetV1 achieve significantimprovement of around 8% for 2 bit quantization and 7% for 1 bit compression.

R^2: Range Regularization for Model Compression and Quantization