@article{Nia_Belbahri_2018, title={Binary Quantizer}, volume={4}, url={https://openjournals.uwaterloo.ca/index.php/vsl/article/view/334}, abstractNote={<p>One-bit quantization is a general tool to execute a complex model,<br>such as deep neural networks, on a device with limited resources,<br>such as cell phones. Naively compressing weights into one bit<br>yields an extensive accuracy loss. One-bit models, therefore, re-<br>quire careful re-training. Here we introduce a class functions de-<br>vised to be used as a regularizer for re-training one-bit models. Us-<br>ing a regularization function, specifically devised for binary quanti-<br>zation, avoids heuristic touch of the optimization scheme and saves<br>considerable coding effort.</p>}, number={1}, journal={Journal of Computational Vision and Imaging Systems}, author={Nia, Vahid Partovi and Belbahri, Mouloud}, year={2018}, month={Dec.}, pages={3} }