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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

Targeted hydrolysis of α-lactalbumin to reduce allergenicity based on the synergistic effect of pepsin and papain

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International journal of biological macromolecules 2025年第Pt 4期311卷 144016页

作者： Qing Zhao Guangqing Mu Yijia Zheng Wenjiao Du Anqi Zhao Qi Sun Peng Sun Xiaomeng Wu Fanhua Kong School of Food Science and Technology Dalian Polytechnic University Dalian 116034 Liaoning China Dalian Key Laboratory of Functional Probiotics School of Food Science and Technology Dalian Polytechnic University Dalian 116034 China. School of Food Science and Technology Dalian Polytechnic University Dalian 116034 Liaoning China. Dalian Key Laboratory of Functional Probiotics School of Food Science and Technology Dalian Polytechnic University Dalian 116034 China Department of Agricultural Food and Nutritional Science University of Alberta Edmonton Alberta T6G 2P5 Canada. SKL of Marine Food Processing & Safety Control National Engineering Research Center of Seafood Collaborative Innovation Center of Seafood Deep Processing National & Local Joint Engineering School of Food Science and Technology Dalian Polytechnic University Dalian 116034 China Laboratory for Marine Bioactive Polysaccharide Development and Application Liaoning Key Laboratory of Food Nutrition and Health School of Food Science and Technology Dalian Polytechnic University Dalian 116034 China. Electronic address: kkong0930@***.

Enzymolysis can effectively reduce the allergenicity of α-lactalbumin (ALA). Herein, complex enzymes targeted enzymolysis technology was developed to reduce the allergenicity of ALA, and the allergenicity of hydrolysates (WM) were investigated from multiple perspectives. The results showed that compared with the single enzyme hydrolysates, the degree of hydrolysis and antigen reduction rate of WM were increased by 5.57 % and 25.31 %, and linear allergenic epitopes were further eliminated. The spatial conformation of WM was disrupted, manifested by decreasing in α-helix content and surface hydrophobicity. In addition, WM could alleviate allergic symptoms in mice, reduced the binding ability with IgE and IgG and the release of mast cells degranulation, maintained the balance of Th1/Th2 cells and intestinal barrier function. Spearman's correlation analysis further revealed the correlation between the relative abundance of marker genus and allergy related indicators. This study will provide theoretical basis for the development of low allergenic dairy-based materials.

关键词： Allergenicity Epitopes Intestinal barrier Targeted enzymolysis α-Lactalbumin

Revisiting distillation and incremental classifier learning

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arXiv 2018年

作者： Javed, Khurram Shafait, Faisal Deep Learning Laboratory National Center of Artificial Intelligence Islamabad Pakistan School of Electrical Engineering and Computer Science National University of Sciences and Technology

One of the key differences between the learning mechanism of humans and Artificial Neural Networks (ANNs) is the ability of humans to learn one task at a time. ANNs, on the other hand, can only learn multiple tasks simultaneously. Any attempts at learning new tasks incrementally cause them to completely forget about previous tasks. This lack of ability to learn incrementally, called Catastrophic Forgetting, is considered a major hurdle in building a true AI system. In this paper, our goal is to isolate the truly effective existing ideas for incremental learning from those that only work under certain condi- tions. To this end, we first thoroughly analyze the current state of the art (iCaRL) method for incremental learning and demonstrate that the good performance of the system is not because of the reasons presented in the existing literature. We conclude that the success of iCaRL is primarily due to knowledge distillation and recognize a key limitation of knowledge distillation, i.e, it often leads to bias in classifiers. Finally, we propose a dynamic threshold moving algorithm that is able to success- fully remove this bias. We demonstrate the effectiveness of our algorithm on CIFAR100 and MNIST datasets showing near-optimal results. Our implementation is available at : https://***/Khurramjaved96/ incremental-learning. Copyright © 2018, The Authors. All rights reserved.

关键词： Neural networks

Out-of-town recommendation with travel intention modeling

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arXiv 2021年

作者： Xin, Haoran Lu, Xinjiang Xu, Tong Liu, Hao Gu, Jingjing Dou, Dejing Xiong, Hui University of Science and Technology of China China Business Intelligence Lab Baidu Research China National Engineering Laboratory of Deep Learning Technology and Application China Nanjing University of Aeronautics and Astronautics China Rutgers University United States

Out-of-town recommendation is designed for those users who leave their home-town areas and visit the areas they have never been to before. It is challenging to recommend Point-of-Interests (POIs) for out-of-town users since the out-of-town check-in behavior is determined by not only the user’s home-town preference but also the user’s travel intention. Besides, the user’s travel intentions are complex and dynamic, which leads to big difficulties in understanding such intentions precisely. In this paper, we propose a TRAvel-INtention-aware Out-of-town Recommendation framework, named TRAINOR. The proposed TRAINOR framework distinguishes itself from existing out-of-town recommenders in three aspects. First, graph neural networks are explored to represent users’ home-town check-in preference and geographical constraints in out-of-town check-in behaviors. Second, a user-specific travel intention is formulated as an aggregation combining home-town preference and generic travel intention together, where the generic travel intention is regarded as a mixture of inherent intentions that can be learned by Neural Topic Model (NTM). Third, a non-linear mapping function, as well as a matrix factorization method, are employed to transfer users’ home-town preference and estimate out-of-town POI’s representation, respectively. Extensive experiments on real-world data sets validate the effectiveness of the TRAINOR framework. Moreover, the learned travel intention can deliver meaningful explanations for understanding a user’s travel purposes. © 2021, CC BY-NC-SA.

关键词： Graph neural networks

Spatial object recommendation with hints: When spatial granularity matters

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arXiv 2021年

作者： Luo, Hui Zhou, Jingbo Bao, Zhifeng Li, Shuangli Culpepper, J. Shane Ying, Haochao Liu, Hao Xiong, Hui RMIT University Australia Business Intelligence Lab Baidu Research National Engineering Laboratory of Deep Learning Technology and Application China University of Science and Technology of China China Zhejiang University China Rutgers University United States

Existing spatial object recommendation algorithms generally treat objects identically when ranking them. However, spatial objects often cover different levels of spatial granularity and thereby are heterogeneous. For example, one user may prefer to be recommended a region (say Manhattan), while another user might prefer a venue (say a restaurant). Even for the same user, preferences can change at different stages of data exploration. In this paper, we study how to support top-k spatial object recommendations at varying levels of spatial granularity, enabling spatial objects at varying granularity, such as a city, suburb, or building, as a Point of Interest (POI). To solve this problem, we propose the use of a POI tree, which captures spatial containment relationships between POIs. We design a novel multi-task learning model called MPR (short for Multi-level POI Recommendation), where each task aims to return the top-k POIs at a certain spatial granularity level. Each task consists of two subtasks: (i) attribute-based representation learning;(ii) interaction-based representation learning. The first subtask learns the feature representations for both users and POIs, capturing attributes directly from their profiles. The second subtask incorporates user-POI interactions into the model. Additionally, MPR can provide insights into why certain recommendations are being made to a user based on three types of hints: user-aspect, POI-aspect, and interaction-aspect. We empirically validate our approach using two real-life datasets, and show promising performance improvements over several state-of-the-art methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Forestry

ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection

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arXiv 2022年

作者： Yin, Junbo Zhou, Dingfu Zhang, Liangjun Fang, Jin Xu, Cheng-Zhong Shen, Jianbing Wang, Wenguan School of Computer Science Beijing Institute of Technology China Baidu Research China National Engineering Laboratory of Deep Learning Technology and Application China SKL-IOTSC CIS University of Macau China ReLER AAII University of Technology Sydney Australia

Existing approaches for unsupervised point cloud pre-training are constrained to either scene-level or point/voxel-level instance discrimination. Scene-level methods tend to lose local details that are crucial for recognizing the road objects, while point/voxel-level methods inherently suffer from limited receptive field that is incapable of perceiving large objects or context environments. Considering region-level representations are more suitable for 3D object detection, we devise a new unsupervised point cloud pre-training framework, called ProposalContrast, that learns robust 3D representations by contrasting region proposals. Specifically, with an exhaustive set of region proposals sampled from each point cloud, geometric point relations within each proposal are modeled for creating expressive proposal representations. To better accommodate 3D detection properties, ProposalContrast optimizes with both inter-cluster and inter-proposal separation, i.e., sharpening the discriminativeness of proposal representations across semantic classes and object instances. The generalizability and transferability of ProposalContrast are verified on various 3D detectors (i.e., PV-RCNN, CenterPoint, PointPillars and PointRCNN) and datasets (i.e., KITTI, Waymo and ONCE). Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

FaceScape: A Large-Scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction

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FaceScape: A Large-Scale High Quality 3D Face Dataset and De...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Haotian Yang Hao Zhu Yanru Wang Mingkai Huang Qiu Shen Ruigang Yang Xun Cao Nanjing University Baidu Research National Engineering Laboratory for Deep Learning Technology and Applications China University of Kentucky Inceptio Inc.

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

In this paper, we present a large-scale detailed 3D face dataset, FaceScape, and propose a novel algorithm that is able to predict elaborate riggable 3D face models from a single image input. FaceScape dataset provides 18,760 textured 3D faces, captured from 938 subjects and each with 20 specific expressions. The 3D models contain the pore-level facial geometry that is also processed to be topologically uniformed. These fine 3D facial models can be represented as a 3D morphable model for rough shapes and displacement maps for detailed geometry. Taking advantage of the large-scale and high-accuracy dataset, a novel algorithm is further proposed to learn the expression-specific dynamic details using a deep neural network. The learned relationship serves as the foundation of our 3D face prediction system from a single image input. Different than the previous methods, our predicted 3D models are riggable with highly detailed geometry under different expressions. The unprecedented dataset and code will be released to public for research purpose.

关键词： Face Three-dimensional displays Solid modeling Shape Geometry Predictive models Cameras

IDARTS: Interactive Differentiable Architecture Search

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IDARTS: Interactive Differentiable Architecture Search

International Conference on Computer Vision (ICCV)

作者： Song Xue Runqi Wang Baochang Zhang Tian Wang Guodong Guo David Doermann Beihang University Beijing China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing China Lobachevsky State University of Nizhni Novgorod Nizhni Novgorod Russian Federation National Engineering Laboratory for Deep Learning Technology and Application Institute of Deep Learning Baidu Research Beijing China University at Buffalo USA

ISBN: (纸本)9781665428132

Differentiable Architecture Search (DARTS) improves the efficiency of architecture search by learning the architecture and network parameters end-to-end. However, the intrinsic relationship between the architecture’s parameters is neglected, leading to a sub-optimal optimization process. The reason lies in the fact that the gradient descent method used in DARTS ignores the coupling relationship of the parameters and therefore degrades the optimization. In this paper, we address this issue by formulating DARTS as a bi-linear optimization problem and introducing an Interactive Differentiable Architecture Search (IDARTS). We first develop a backtracking backpropagation process, which can decouple the relationships of different kinds of parameters and train them in the same framework. The backtracking method coordinates the training of different parameters that fully explore their interaction and optimize training. We present experiments on the CIFAR10 and ImageNet datasets that demonstrate the efficacy of the IDARTS approach by achieving a top-1 accuracy of 76.52% on ImageNet without additional search cost vs. 75.8% with the state-of-the-art PC-DARTS.

关键词： Training Couplings Backpropagation Backtracking Computer vision Costs Computer architecture