LSQ加通过可学习的偏移量和更好的初始化来改善低位量化 Unlike ReLU, newer activation functions (like Swish, H-swish, Mish) that are frequently employed in popular efficient architectures can also result in
有效的神经网络设计的结构化卷积 In this work, we tackle model efficiency by exploiting redundancy in the \textit{implicit structure} of the building blocks of convolutional neural ne