text: 297,85,1138318389804322816,826924323113807872,Mohammad Rastegari,"Depth-wise Convolution and Point-wise Convolution showed tremendous benefit for efficient architecture design and now we are presenting a new efficient CNN model DiCENet based on Dimension-wise convolution #deeplearning , #ComputerVision paper: <LINK> <LINK>",https://arxiv.org/abs/1906.03516,"We introduce a novel and generic convolutional unit, DiCE unit, that is built using dimension-wise convolutions and dimension-wise fusion. The dimension-wise convolutions apply light-weight convolutional filtering across each dimension of the input tensor while dimension-wise fusion efficiently combines these dimension-wise representations; allowing the DiCE unit to efficiently encode spatial and channel-wise information contained in the input tensor. The DiCE unit is simple and can be seamlessly integrated with any architecture to improve its efficiency and performance. Compared to depth-wise separable convolutions, the DiCE unit shows significant improvements across different architectures. When DiCE units are stacked to build the DiCENet model, we observe significant improvements over state-of-the-art models across various computer vision tasks including image classification, object detection, and semantic segmentation. On the ImageNet dataset, the DiCENet delivers 2-4% higher accuracy than state-of-the-art manually designed models (e.g., MobileNetv2 and ShuffleNetv2). Also, DiCENet generalizes better to tasks (e.g., object detection) that are often used in resource-constrained devices in comparison to state-of-the-art separable convolution-based efficient networks, including neural search-based methods (e.g., MobileNetv3 and MixNet. Our source code in PyTorch is open-source and is available at this https URL ",DiCENet: Dimension-wise Convolutions for Efficient Networks,1,"['Depth-wise Convolution and Point-wise Convolution showed tremendous benefit for efficient architecture design and now we are presenting  a new efficient CNN model DiCENet based on  Dimension-wise convolution #deeplearning , #ComputerVision \npaper:  <LINK> <LINK>']",19,06,258