f8e59f4b2fe7c5705bf878bbd494ccdf-AuthorFeedback.pdf

However, such an implementation showed roughly 10-20% drop in performance compared to the current design. MetaQuant costs 51.15 seconds to finish We will add this training time analysis in final version. MetaQuant focuses more on how to improve STE-based training quantization, without any extra loss and training tricks. MetaQuant follows dorefa using a symmetric quantization which leads to efficient inference. Regarding "... there seems to be a chicken-egg problem", the meta quantizer is actually linked to the final loss L of the Regarding "... should the loss function of the base network be used for training...", note that the goal of base network is to minimize the final prediction loss while the aim of the meta quantizer is to provide accurate gradient L/ Ŵ. Ideally, That's why STE is used to approximate the gradients in previous methods.