Recently, there have been tremendous efforts in developing lightweight Deep Neural Networks (DNNs) with satisfactory accuracy, which can enable the ubiquitous deployment of DNNs in edge devices. The core challenge of developing compact and efficient DNNs lies in how to balance the competing goals of achieving high accuracy and high efficiency. In this paper we propose two novel types of convolutions, dubbed \emph{Pixel Difference Convolution (PDC) and Binary PDC (Bi-PDC)} which enjoy the following benefits: capturing higher-order local differential information, computationally efficient, and able to be integrated with existing DNNs. With PDC and Bi-PDC, we further present two lightweight deep networks named \emph{Pixel Difference Networks (PiDiNet)} and \emph{Binary PiDiNet (Bi-PiDiNet)} respectively to learn highly efficient yet more accurate representations for visual tasks including edge detection and object recognition. Extensive experiments on popular datasets (BSDS500, ImageNet, LFW, YTF, \emph{etc.}) show that PiDiNet and Bi-PiDiNet achieve the best accuracy-efficiency trade-off. For edge detection, PiDiNet is the first network that can be trained without ImageNet, and can achieve the human-level performance on BSDS500 at 100 FPS and with $<$1M parameters. For object recognition, among existing Binary DNNs, Bi-PiDiNet achieves the best accuracy and a nearly $2\times$ reduction of computational cost on ResNet18. Code available at \href{https://github.com/hellozhuo/pidinet}{https://github.com/hellozhuo/pidinet}.

提出两种新型卷积方法Pixel Difference Convolution（PDC）和Binary PDC（Bi-PDC），以及两个轻量级深度网络PiDiNet和Bi-PiDiNet用于视觉任务，如边缘检测和目标识别。在BSDS500、ImageNet、LFW和YTF等数据集上进行的广泛实验表明，PiDiNet和Bi-PiDiNet在准确性和效率之间取得了最佳的平衡。对于边缘检测，PiDiNet是第一个可以在没有ImageNet的情况下进行训练的网络，在BSDS500上以100 FPS和小于1M参数实现了与人类水平相当的性能。对于目标识别，Bi-PiDiNet在现有的二进制DNN中取得了最佳准确性，并在ResNet18上将计算成本减少了近2倍。

轻量级像素差异网络用于高效视觉表示学习