Binary Convolutional Neural Networks (CNNs) have significantly reduced the number of arithmetic operations and the size of memory storage needed for CNNs, which makes their deployment on mobile and embedded systems more feasible. However, the CNN architecture after binarizing requires to be redesigned and refined significantly due to two reasons: 1. the large accumulation error of binarization in the forward propagation, and 2. the severe gradient mismatch problem of binarization in the backward propagation. Even though the substantial effort has been invested in designing architectures for single and multiple binary CNNs, it is still difficult to find an optimal architecture for binary CNNs. In this paper, we propose a strategy, named NASB, which adopts Neural Architecture Search (NAS) to find an optimal architecture for the binarization of CNNs. Due to the flexibility of this automated strategy, the obtained architecture is not only suitable for binarization but also has low overhead, achieving a better trade-off between the accuracy and computational complexity of hand-optimized binary CNNs. The implementation of NASB strategy is evaluated on the ImageNet dataset and demonstrated as a better solution compared to existing quantized CNNs. With the insignificant overhead increase, NASB outperforms existing single and multiple binary CNNs by up to 4.0% and 1.0% Top-1 accuracy respectively, bringing them closer to the precision of their full precision counterpart. The code and pretrained models will be publicly available.
It is challenging to bridge the performance gap between Binary CNN (BCNN) and Floating point CNN (FCNN). We observe that, this performance gap leads to substantial residuals between intermediate feature maps of BCNN and FCNN. To minimize the performance gap, we enforce BCNN to produce similar intermediate feature maps with the ones of FCNN. This training strategy, i.e., optimizing each binary convolutional block with block-wise distillation loss derived from FCNN, leads to a more effective optimization to BCNN. It also motivates us to update the binary convolutional block architecture to facil…
Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization
Institute of Software Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）；JD Digits（京东数科）；University at Albany, State University of New York（纽约州立大学奥尔巴尼分校）；Beihang University（北京航空航天大学）；Tencent Youtu Lab（腾讯优图实验室）
Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification
Aalto University School of Science（阿尔托大学科学学院）；Linköping University（林雪平大学）；Universitat Autonoma de Barcelona（巴塞罗那自治大学）；VTT Technical Research Centre of Finland Ltd（芬兰VTT技术研究中心有限公司）
This paper studies the problem of designing compact binary architectures for vision multi-layer perceptrons (MLPs). We provide extensive analysis on the difficulty of binarizing vision MLPs and find that previous binarization methods perform poorly due to limited capacity of binary MLPs. In contrast with the traditional CNNs that utilizing convolutional operations with large kernel size, fully-connected (FC) layers in MLPs can be treated as convolutional layers with kernel size $1\times1$. Thus, the representation ability of the FC layers will be limited when being binarized, and places restri…
Student engagement is an important factor in meeting the goals of virtual learning programs. Automatic measurement of student engagement provides helpful information for instructors to meet learning program objectives and individualize program delivery. Many existing approaches solve video-based engagement measurement using the traditional frameworks of binary classification (classifying video snippets into engaged or disengaged classes), multi-class classification (classifying video snippets into multiple classes corresponding to different levels of engagement), or regression (estimating a co…
National Taiwan University of Science and Technology（国立台湾科技大学）
Pruning redundant filters in CNN models has received growing attention. In this paper, we propose an adaptive binary search-first hybrid pyramid- and clustering-based (ABSHPC-based) method for pruning filters automatically. In our method, for each convolutional layer, initially a hybrid pyramid data structure is constructed to store the hierarchical information of each filter. Given a tolerant accuracy loss, without parameters setting, we begin from the last convolutional layer to the first layer; for each considered layer with less or equal pruning rate relative to its previous layer, our ABS…
Russian Research Center Huawei Technologies, Russia（华为技术有限公司俄罗斯研究中心）；Department of Computer Science ETH Zurich, Switzerland（苏黎世联邦理工学院计算机科学系）
Despite the growing popularity of deep learning technologies, high memory requirements and power consumption are essentially limiting their application in mobile and IoT areas. While binary convolutional networks can alleviate these problems, the limited bitwidth of weights is often leading to significant degradation of prediction accuracy. In this paper, we present a method for training binary networks that maintains a stable predefined level of their information capacity throughout the training process by applying Shannon entropy based penalty to convolutional filters. The results of experim…
Binary Convolutional Neural Networks (CNNs) can significantly reduce the number of arithmetic operations and the size of memory storage, which makes the deployment of CNNs on mobile or embedded systems more promising. However, the accuracy degradation of single and multiple binary CNNs is unacceptable for modern architectures and large scale datasets like ImageNet. In this paper, we proposed a Piecewise Approximation (PA) scheme for multiple binary CNNs which lessens accuracy loss by approximating full precision weights and activations efficiently and maintains parallelism of bitwise operation…
Binary convolutional networks have lower computational load and lower memory foot-print compared to their full-precision counterparts. So, they are a feasible alternative for the deployment of computer vision applications on limited capacity embedded devices. Once trained on less resource-constrained computational environments, they can be deployed for real-time inference on such devices. In this study, we propose an implementation of binary convolutional network inference on GPU by focusing on optimization of XNOR convolution. Experimental results show that using GPU can provide a speed-up of…
Delft University of Technology（代尔夫特理工大学）；IBM Research（IBM研究院）；Yonsei University（延世大学）
Binary Convolutional Neural Networks (BCNNs) can significantly improve the efficiency of Deep Convolutional Neural Networks (DCNNs) for their deployment on resource-constrained platforms, such as mobile and embedded systems. However, the accuracy degradation of BCNNs is still considerable compared with their full precision counterpart, impeding their practical deployment. Because of the inevitable binarization error in the forward propagation and gradient mismatch problem in the backward propagation, it is nontrivial to train BCNNs to achieve satisfactory accuracy. To ease the difficulty of tr…
From Selective Deep Convolutional Features to Compact Binary Representations for Image Retrieval
University of Liverpool（利物浦大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Queensland University of Technology（昆士兰科技大学）；University of Dayton（代顿大学）
Image ordinal estimation is to predict the ordinal label of a given image, which can be categorized as an ordinal regression problem. Recent methods formulate an ordinal regression problem as a series of binary classification problems. Such methods cannot ensure that the global ordinal relationship is preserved since the relationships among different binary classifiers are neglected. We propose a novel ordinal regression approach, termed Convolutional Ordinal Regression Forest or CORF, for image ordinal estimation, which can integrate ordinal regression and differentiable decision trees with a…
Deep Convolutional Neural Networks (CNN) have been successfully applied to many real-life problems. However, the huge memory cost of deep CNN models poses a great challenge of deploying them on memory-constrained devices (e.g., mobile phones). One popular way to reduce the memory cost of deep CNN model is to train binary CNN where the weights in convolution filters are either 1 or -1 and therefore each weight can be efficiently stored using a single bit. However, the compression ratio of existing binary CNN models is upper bounded by around 32. To address this limitation, we propose a novel me…
The traffic video data has become a critical factor in confining the state of traffic congestion due to the recent advancements in computer vision. This work proposes a unique technique for traffic video classification using a color-coding scheme before training the traffic data in a Deep convolutional neural network. At first, the video data is transformed into an imagery data set; then, the vehicle detection is performed using the You Only Look Once algorithm. A color-coded scheme has been adopted to transform the imagery dataset into a binary image dataset. These binary images are fed to a …
Light binary convolutional neural networks (LB-CNN) are particularly useful when implemented in low-energy computing platforms as required in many industrial applications. Herein, a framework for optimizing compact LB-CNN is introduced and its effectiveness is evaluated. The framework is freely available and may run on free-access cloud platforms, thus requiring no major investments. The optimized model is saved in the standardized .h5 format and can be used as input to specialized tools for further deployment into specific technologies, thus enabling the rapid development of various intellige…
Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）
In this paper, we propose binary sparse convolutional networks called BSC-Net for efficient point cloud analysis. We empirically observe that sparse convolution operation causes larger quantization errors than standard convolution. However, conventional network quantization methods directly binarize the weights and activations in sparse convolution, resulting in performance drop due to the significant quantization loss. On the contrary, we search the optimal subset of convolution operation that activates the sparse convolution at various locations for quantization error alleviation, and the pe…
A GPU-Outperforming FPGA Accelerator Architecture for Binary Convolutional Neural Networks
Arizona State University（亚利桑那州立大学）；Nanyang Technological University（南洋理工大学）
基于FPGA的卷积神经网络（CNN）硬件加速器由于其比GPU更高的能量效率而受到广泛关注。然而，基于FPGA的解决方案要实现比GPU更高的吞吐量是具有挑战性的。在本文中，我们证明了当CNN在权重和激活的二进制约束下进行训练时，FPGA加速在吞吐量和能效方面都是一种优越的解决方案。具体来说，我们提出了一种优化的FPGA加速器架构，专为逐位卷积和归一化而设计，具有大规模的空间并行性和深流水线阶段。FPGA加速器的一个关键优势是其性能对数据批量大小不敏感，而GPU加速的性能在很大程度上取决于数据的批量大小。实验结果表明，在Virtex-7 FPGA上运行的二进制CNN的加速器架构比Titan X GPU快8.3倍，能效高75倍，可以处理小批量的在线个人请求。对于处理大批量静态数据，该解决方案在吞吐量方面与Titan X GPU不相上下，同时提供9.5倍的能效。
Southeast University（东南大学）；INSERM（法国国家健康与医学研究院）；Université de Rennes 1（雷恩第一大学）；Centre de Recherche en Information Médicale Sino-français (CRIBs)（中法医学信息研究中心（CRIBs））
In the low-bit quantization field, training Binary Neural Networks (BNNs) is the extreme solution to ease the deployment of deep models on resource-constrained devices, having the lowest storage cost and significantly cheaper bit-wise operations compared to 32-bit floating-point counterparts. In this paper, we introduce Sub-bit Neural Networks (SNNs), a new type of binary quantization design tailored to compress and accelerate BNNs. SNNs are inspired by an empirical observation, showing that binary kernels learnt at convolutional layers of a BNN model are likely to be distributed over kernel s…
A Deep Convolutional Neural Network for Lung Cancer Diagnostic
Universitat Rovira i Virgili（罗维拉-威尔吉利大学）；University of Cagliari（卡利亚里大学）
在本文中，我们研究了深度学习技术在医学图像分析问题上诊断肺癌的优势。卷积神经网络（CNN）模型由于其在生成高层次图像表示方面的良好效果而成为模式识别和计算机视觉研究领域的热门。我们提出了一种新的深度学习架构，用于学习高级图像表示，以在医学图像二值分类任务中实现低方差的高分类精度。我们的目标是在深度卷积神经网络的开始学习判别紧凑特征。我们在Kaggle Data Science Bowl 2017（KDSB17）数据集上评估了我们的模型，并将其与Kaggle竞赛中提出的一些相关作品进行了比较。
Payame Noor University（帕亚米努尔大学）；University of Tabriz（大不里士大学）
The new Coronavirus is spreading rapidly, and it has taken the lives of many people so far. The virus has destructive effects on the human lung, and early detection is very important. Deep Convolution neural networks are such powerful tools in classifying images. Therefore, in this paper, a hybrid approach based on a deep network is presented. Feature vectors were extracted by applying a deep convolution neural network on the images, and useful features were selected by the binary differential meta-heuristic algorithm. These optimized features were given to the SVM classifier. A database consi…
Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks
Institute for Medical Engineering and Science（医学工程与科学研究所）；Massachusetts Institute of Technology（麻省理工学院）；Lyft Inc.（来福特公司）；Neuromation OU（神经自动化有限公司）；University of Michigan（密歇根大学）
In this paper, we design lightweight graph convolutional networks (GCNs) using a particular class of regularizers, dubbed as phase-field models (PFMs). PFMs exhibit a bi-phase behavior using a particular ultra-local term that allows training both the topology and the weight parameters of GCNs as a part of a single "end-to-end" optimization problem. Our proposed solution also relies on a reparametrization that pushes the mask of the topology towards binary values leading to effective topology selection and high generalization while implementing any targeted pruning rate. Both masks and weights …
Application of Quantum Convolutional Neural Networks for MRI-Based Brain Tumor Detection and Classification
IPB University（茂物农业大学）；Nguyen Tat Thanh University（阮必成大学）；Princess Nourah bint Abdulrahman University（努拉·宾特·阿卜杜勒拉赫曼公主大学）；King Saud Bin Abdulaziz University for Health Sciences（沙特·本·阿卜杜勒阿齐兹国王健康科学大学）；King Faisal Specialist Hospital & Research Center（费萨尔国王专科医院与研究中心）
