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Model compression has been introduced to reduce the required hardwareresources while maintaining the model accuracy. Lots of techniques for modelcompression, such as pruning, quantization, and low-rank approximation, havebeen suggested along with different inference implementation characteristics.Adopting model compression is, however, still challenging because the designcomplexity of model compression is rapidly increasing due to additionalhyper-parameters and computation overhead in order to achieve a highcompression ratio. In this paper, we propose a simple and efficient modelcompression framework called DeepTwist which distorts weights in an occasionalmanner without modifying the underlying training algorithms. The ideas ofdesigning weight distortion functions are intuitive and straightforward givenformats of compressed weights. We show that our proposed framework improvescompression rate significantly for pruning, quantization, and low-rankapproximation techniques while the efforts of additional retraining and/orhyper-parameter search are highly reduced. Regularization effects of DeepTwistare also reported.

DeepTwist: Learning Model Compression via Occasional Weight Distortion