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 https://github.com/sacmehta/EdgeNets/

翻译：我们引入了一个新颖的、通用的共变单元DICE 单元, 这个单元是使用维度- 维度- 共变和维度- 融合来构建的。 维度- 维度- 共变在输入点的每个维度上应用轻量级的共变过滤器, 而维度- 共变将这些维度- 高效地结合这些维度- 表达式; 允许 DICE 单位高效地编码输入输入点的空和频道- 信息。 DICE 单元简单, 并且可以与任何架构无缝地整合, 以提高其效率和性能。 与深度- 分相异的共变组合相比, DICE 单元显示不同结构之间的重大改进。 当 DICE 单位堆叠以构建 DCENet 模型的每个维度- 轻度- 共变异性过滤器时, 我们观察到了各种计算机视觉任务, 包括图像分类, 对象/ 网络 网络 和网络 内流- 系统- 系统- 系统- 系统- 搜索系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统- 系统