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Intelligent exploration for user interface modules of mobile app with collective learning

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arXiv 2020年

作者： Zhou, Jingbo Tang, Zhenwei Zhao, Min Ge, Xiang Zhuang, Fuzhen Zhou, Meng Zou, Liming Yang, Chenglei Xiong, Hui Business Intelligence Lab Baidu Research Baidu TPG User Experience Department China National Engineering Laboratory of Deep Learning Technology and Application China Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Beijing University of Posts and Telecommunications China Peking University China Shandong University China Rutgers University United States

A mobile app interface usually consists of a set of user interface modules. How to properly design these user interface modules is vital to achieving user satisfaction for a mobile app. However, there are few methods to determine design variables for user interface modules except for relying on the judgment of designers. Usually, a laborious post-processing step is necessary to verify the key change of each design variable. Therefore, there is a only very limited amount of design solutions that can be tested. It is time-consuming and almost impossible to figure out the best design solutions as there are many modules. To this end, we introduce FEELER, a framework to fast and intelligently explore design solutions of user interface modules with a collective machine learning approach. FEELER can help designers quantitatively measure the preference score of different design solutions, aiming to facilitate the designers to conveniently and quickly adjust user interface module. We conducted extensive experimental evaluations on two real-life datasets to demonstrate its applicability in real-life cases of user interface module design in the Baidu App, which is one of the most popular mobile apps in China. Copyright © 2020, The Authors. All rights reserved.

关键词： User interfaces

Every Pixel Counts: Unsupervised geometry learning with holistic 3d motion understanding

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arXiv 2018年

作者： Yang, Zhenheng Wang, Peng Wang, Yang Xu, Wei Nevatia, Ram University of Southern California Baidu Research National Engineering Laboratory for Deep Learning Technology and Applications

learning to estimate 3D geometry in a single image by watching unlabeled videos via deep convolutional network has made significant process recently. Current state-of-the-art (SOTA) methods, are based on the learning framework of rigid structure-from-motion, where only 3D camera ego motion is modeled for geometry estimation. However, moving objects also exist in many videos, e.g. moving cars in a street scene. In this paper, we tackle such motion by additionally incorporating per-pixel 3D object motion into the learning framework, which provides holistic 3D scene flow understanding and helps single image geometry estimation. Specifically, given two consecutive frames from a video, we adopt a motion network to predict their relative 3D camera pose and a segmentation mask distinguishing moving objects and rigid background. An optical flow network is used to estimate dense 2D per-pixel correspondence. A single image depth network predicts depth maps for both images. The four types of information, i.e. 2D flow, camera pose, segment mask and depth maps, are integrated into a differentiable holistic 3D motion parser (HMP), where per-pixel 3D motion for rigid background and moving objects are recovered. We design various losses w.r.t. the two types of 3D motions for training the depth and motion networks, yielding further error reduction for estimated geometry. Finally, in order to solve the 3D motion confusion from monocular videos, we combine stereo images into joint training. Experiments on KITTI 2015 dataset show that our estimated geometry, 3D motion and moving object masks, not only are constrained to be consistent, but also significantly outperforms other SOTA algorithms, demonstrating the benefits of our approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Pixels

Generalizing from a few examples: A survey on few-shot learning

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arXiv 2019年

作者： WANG, YAQING YAO, QUANMING KWOK, JAMES T. NI, LIONEL M. Department of Computer Science and Engineering Hong Kong University of Science and Technology Business Intelligence Lab National Engineering Laboratory of Deep Learning Technology and Application Baidu Research 4Paradigm Inc.

Machine learning has been highly successful in data-intensive applications, but is often hampered when the data set is small. Recently, Few-Shot learning (FSL) is proposed to tackle this problem. Using prior knowledge, FSL can rapidly generalize to new tasks containing only a few samples with supervised information. In this paper, we conduct a thorough survey to fully understand FSL. Starting from a formal definition of FSL, we distinguish FSL from several relevant machine learning problems. We then point out that the core issue in FSL is that the empirical risk minimizer is unreliable. Based on how prior knowledge can be used to handle this core issue, we categorize FSL methods from three perspectives: (i) data, which uses prior knowledge to augment the supervised experience;(ii) model, which uses prior knowledge to reduce the size of the hypothesis space;and (iii) algorithm, which uses prior knowledge to alter the search for the best hypothesis in the given hypothesis space. With this taxonomy, we review and discuss the pros and cons of each category. Promising directions, in the aspects of the FSL problem setups, techniques, applications and theories, are also proposed to provide insights for future research. Copyright © 2019, The Authors. All rights reserved.

关键词： Surveys

RBCN: Rectified Binary convolutional networks for enhancing the Performance of 1-bit DCNNs

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arXiv 2019年

作者： Liu, Chunlei Ding, Wenrui Xia, Xin Hu, Yuan Zhang, Baochang Liu, Jianzhuang Zhuang, Bohan Guo, Guodong School of Electronic and Information Engineering Beihang University Unmanned System Research Institute Beihang University School of Automation Science and Electrical Engineering Beihang University Huawei Noah's Ark Lab University of Adelaide Institute of Deep Learning Baidu Research National Engineering Laboratory for Deep Learning Technology and Application

Binarized convolutional neural networks (BCNNs) are widely used to improve memory and computation efficiency of deep convolutional neural networks (DCNNs) for mobile and AI chips based applications. However, current BCNNs are not able to fully explore their corresponding full-precision models, causing a significant performance gap between them. In this paper, we propose rectified binary convolutional networks (RBCNs), towards optimized BCNNs, by combining full-precision kernels and feature maps to rectify the binarization process in a unified framework. In particular, we use a GAN to train the 1-bit binary network with the guidance of its corresponding full-precision model, which significantly improves the performance of BCNNs. The rectified convolutional layers are generic and flexible, and can be easily incorporated into existing DCNNs such as WideResNets and ResNets. Extensive experiments demonstrate the superior performance of the proposed RBCNs over state-of-the-art BCNNs. In particular, our method shows strong generalization on the object tracking task. Copyright © 2019, The Authors. All rights reserved.

关键词： deep neural networks

LEGO: learning edge with geometry all at once by watching videos

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arXiv 2018年

作者： Yang, Zhenheng Wang, Peng Wang, Yang Xu, Wei Nevatia, Ram University of Southern California Baidu Research National Engineering Laboratory for Deep Learning Technology and Applications

learning to estimate 3D geometry in a single image by watching unlabeled videos via deep convolutional network is attracting significant attention. In this paper, we introduce a "3D as-smooth-as-possible (3D-ASAP)" prior inside the pipeline, which enables joint estimation of edges and 3D scene, yielding results with significant improvement in accuracy for fine detailed structures. Specifically, we define the 3D-ASAP prior by requiring that any two points recovered in 3D from an image should lie on an existing planar surface if no other cues provided. We design an unsupervised framework that Learns Edges and Geometry (depth, normal) all at Once (LEGO). The predicted edges are embedded into depth and surface normal smoothness terms, where pixels without edges in-between are constrained to satisfy the prior. In our framework, the predicted depths, normals and edges are forced to be consistent all the time. We conduct experiments on KITTI to evaluate our estimated geometry and CityScapes to perform edge evaluation. We show that in all of the tasks, ***, normal and edge, our algorithm vastly outperforms other state-of-the-art (SOTA) algorithms, demonstrating the benefits of our approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Geometry

Semi-supervised hierarchical recurrent graph neural network for city-wide parking availability prediction

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arXiv 2019年

作者： Zhang, Weijia Liu, Hao Liu, Yanchi Zhou, Jingbo Xiong, Hui University of Science and Technology of China Hefei China Business Intelligence Lab Baidu Research National Engineering Laboratory of Deep Learning Technology and Application Beijing China Rutgers University United States

The ability to predict city-wide parking availability is crucial for the successful development of Parking Guidance and Information (PGI) systems. Indeed, the effective prediction of city-wide parking availability can improve parking efficiency, help urban planning, and ultimately alleviate city congestion. However, it is a non-trivial task for predicting citywide parking availability because of three major challenges: 1) the non-Euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e.g., camera, ultrasonic sensor, and GPS). To this end, we propose Semi-supervised Hierarchical Recurrent Graph Neural Network (SHARE) for predicting city-wide parking availability. Specifically, we first propose a hierarchical graph convolution structure to model non-Euclidean spatial autocorrelation among parking lots. Along this line, a contextual graph convolution block and a soft clustering graph convolution block are respectively proposed to capture local and global spatial dependencies between parking lots. Additionally, we adopt a recurrent neural network to incorporate dynamic temporal dependencies of parking lots. Moreover, we propose a parking availability approximation module to estimate missing real-time parking availabilities from both spatial and temporal domain. Finally, experiments on two real-world datasets demonstrate the prediction performance of SHARE outperforms seven state-of-the-art baselines. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

A model-guided deep network for limited-angle computed tomography

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arXiv 2020年

作者： Wang, Wei Xia, Xiang-Gen He, Chuanjiang Ren, Zemin Lu, Jian Wang, Tianfu Lei, Baiying School of Biomedical Engineering Shenzhen University National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States College of Mathematics and Statistics Chongqing University Chongqing China College of Mathematics and Physics Chongqing University of Science and Technology Chongqing China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen China

In this paper, we first propose a variational model for the limited-angle computed tomography (CT) image reconstruction and then convert the model into an end-to-end deep network. We use the penalty method to solve the model and divide it into three iterative subproblems, where the first subproblem completes the sinograms by utilizing the prior information of sinograms in the frequency domain and the second refines the CT images by using the prior information of CT images in the spatial domain, and the last merges the outputs of the first two subproblems. In each iteration, we use the convolutional neural networks (CNNs) to approxiamte the solutions of the first two subproblems and, thus, obtain an end-to-end deep network for the limited-angle CT image reconstruction. Our network tackles both the sinograms and the CT images, and can simultaneously suppress the artifacts caused by the incomplete data and recover fine structural information in the CT images. Experimental results show that our method outperforms the existing algorithms for the limited-angle CT image reconstruction. Copyright © 2020, The Authors. All rights reserved.

关键词： Computerized tomography

Bayesian Optimized 1-Bit CNNs

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Bayesian Optimized 1-Bit CNNs

International Conference on Computer Vision (ICCV)

作者： Jiaxin Gu Junhe Zhao Xiaolong Jiang Baochang Zhang Jianzhuang Liu Guodong Guo Rongrong Ji Beihang University Beijing China Institute of Deep Learning Baidu Research Beijing China National Engineering Laboratory for Deep Learning Technology and Application Huawei Noah’s Ark Lab China School of Information Science and Engineering Xiamen University Fujian China Peng Cheng Lab Shenzhen China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

deep convolutional neural networks (DCNNs) have dominated the recent developments in computer vision through making various record-breaking models. However, it is still a great challenge to achieve powerful DCNNs in resource-limited environments, such as on embedded devices and smart phones. Researchers have realized that 1-bit CNNs can be one feasible solution to resolve the issue; however, they are baffled by the inferior performance compared to the full-precision DCNNs. In this paper, we propose a novel approach, called Bayesian optimized 1-bit CNNs (denoted as BONNs), taking the advantage of Bayesian learning, a well-established strategy for hard problems, to significantly improve the performance of extreme 1-bit CNNs. We incorporate the prior distributions of full-precision kernels and features into the Bayesian framework to construct 1-bit CNNs in an end-to-end manner, which have not been considered in any previous related methods. The Bayesian losses are achieved with a theoretical support to optimize the network simultaneously in both continuous and discrete spaces, aggregating different losses jointly to improve the model capacity. Extensive experiments on the ImageNet and CIFAR datasets show that BONNs achieve the best classification performance compared to state-of-the-art 1-bit CNNs.

关键词： Bayes methods Kernel Quantization (signal) Convolution Training Frequency modulation Indexes

Bayesian optimized 1-Bit CNNs

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arXiv 2019年

作者： Gu, Jiaxin Zhao, Junhe Jiang, Xiaolong Zhang, Baochang Liu, Jianzhuang Guo, Guodong Ji, Rongrong Beihang University Beijing China Institute of Deep Learning Baidu Research Beijing China National Engineering Laboratory for Deep Learning Technology and Application Huawei Noah's Ark Lab China School of Information Science and Engineering Xiamen University Fujian China Peng Cheng Lab Shenzhen China

deep convolutional neural networks (DCNNs) have dominated the recent developments in computer vision through making various record-breaking models. However, it is still a great challenge to achieve powerful DCNNs in resource-limited environments, such as on embedded devices and smart phones. Researchers have realized that 1-bit CNNs can be one feasible solution to resolve the issue;however, they are baffled by the inferior performance compared to the full-precision DCNNs. In this paper, we propose a novel approach, called Bayesian optimized 1-bit CNNs (denoted as BONNs), taking the advantage of Bayesian learning, a well-established strategy for hard problems, to significantly improve the performance of extreme 1-bit CNNs. We incorporate the prior distributions of full-precision kernels and features into the Bayesian framework to construct 1-bit CNNs in an end-to-end manner, which have not been considered in any previous related methods. The Bayesian losses are achieved with a theoretical support to optimize the network simultaneously in both continuous and discrete spaces, aggregating different losses jointly to improve the model capacity. Extensive experiments on the ImageNet and CIFAR datasets show that BONNs achieve the best classification performance compared to state-of-the-art 1-bit CNNs. Copyright © 2019, The Authors. All rights reserved.

关键词： deep neural networks