Depthwise Multiception Convolution for Reducing Network Parameters without Sacrificing Accuracy
Depthwise Multiception Convolution for Reducing Network Parameters without Sacrificing Accuracy
Deep convolutional neural networks have been proven successful in multiple benchmark challenges in recent years. However, the performance improvements are heavily reliant on increasingly complex network architecture and a high number of parameters, which require ever increasing amounts of storage and memory capacity. Depthwise separable convolution (DSConv) can effectively reduce …