Towards Efficient Deep Learning For Computer Vision
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Towards Efficient Deep Learning For Computer Vision
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Author : Sudhanshu Mittal
language : en
Publisher:
Release Date : 2023*
Towards Efficient Deep Learning For Computer Vision written by Sudhanshu Mittal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023* with categories.
Abstract: Deep learning models require significant resources to deploy, limiting their widespread adoption. The aim of this thesis is to address this issue by proposing methods to make deep learning models more efficient for training and deployment. One important aspect of machine learning is the ability to understand visual information from limited labeled data because large-scale annotation processes can be very expensive or infeasible. The first part of the thesis proposes methods to improve label efficiency for deep learning-based computer vision tasks focusing on two approaches - semi-supervised learning and active learning. For semi-supervised learning, the thesis proposes an approach for semi-supervised semantic segmentation that learns from limited pixel-wise annotated samples while exploiting additional annotation-free images. The proposed dual-branch approach reduces both the low-level and high-level artifacts typically encountered when training with few labels, and its effectiveness is demonstrated on several standard benchmarks. For active learning, the thesis emphasizes that conventional evaluation schemes used in deep active learning are either incomplete or below par. The thesis investigates several existing methods across many dimensions and finds that the studied new underlying factors are decisive in selecting the best active learning approach. The thesis also provides a comprehensive usage guide to obtain the best performance for each case. This thesis covers active learning methods for image classification and semantic segmentation tasks. Another issue with deep neural networks is catastrophic forgetting when encountering new or evolving tasks in a sequential manner. The model must be retrained with all the data or tasks encountered to avoid forgetting, thus making them unsuitable for many real-world applications. The second part of the thesis focuses on understanding and resolving catastrophic forgetting in continual learning, particularly in the Class-incremental Learning (CIL) setting. The evaluation shows that a combination of simple components can already resolve catastrophic forgetting to the same extent as more complex measures proposed in the literature. Overall, this thesis provides streamlined approaches to improve the efficiency of deep learning systems and highlights the importance of many unexplored directions for improved realistic evaluation
Towards Efficient Deep Learning Models For Image Analysis
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Author : Jinnian Zhang
language : en
Publisher:
Release Date : 2022
Towards Efficient Deep Learning Models For Image Analysis written by Jinnian Zhang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Deep learning has revolutionized computer vision and continues to develop rapidly. As deep learning models are more widely applied to solving various problems, practitioners have imposed more requirements on their efficiency, such as model size, inference speed, accuracy, and adversarial robustness. Generally, more efficient models can be obtained by improving the training method or optimizing deep learning architectures. The first part of this dissertation attempts to modify existing vision transformer (ViT) architectures and design a novel two-phase knowledge distillation framework to obtain smaller, faster, and more accurate ViTs for image classification and regression. Our proposed ViTs also show better transferability in downstream tasks such as object detection. In the second part, we focus on the adversarial robustness of U-Nets, which are popular in medical image segmentation and synthesis. We demonstrate that U-Nets are vulnerable to adversarial attacks, such as the Fast Gradient Sign Method (FGSM), in both tasks. To robustify U-Nets, we not only explore commonly used robust training methods (adversarial training and knowledge distillation), but also propose a neural architecture search method to automatically identify robust architectures. These robust U-Net architectures can achieve high robustness using regular training methods, thus avoiding the sacrifice of accuracy usually brought by robust training methods. Our contributions facilitate the widespread adoption of deep learning models in resource-constrained or security-critical applications.
Deep Learning For Image Recognition
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Author : Peng Long
language : en
Publisher: Elsevier
Release Date : 2025-11-03
Deep Learning For Image Recognition written by Peng Long and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-03 with Technology & Engineering categories.
Deep Learning for Image Recognition provides a detailed explanation of the fundamental theories underpinning image recognition and code for recognition tasks in specific application scenarios. Readers can manipulate the existing code, thereby deepening their understanding. Chapters include project work enabling readers to apply the skills and knowledge gained from that section of the book. Projects are based on the accessible Pytorch framework, which is straightforward to learn and can be replicated and modified. Readers are presented with current research findings and up to date techniques in image recognition and deep learning. - A comprehensive introduction to the technology and applications of image recognition based on deep learning - Delves into the core concepts of image recognition, from pre-processing to modelling and algorithm implementation. This is supported by clear descriptions of neural networks, including convolutional neural network principles, model visualization, model compression and model deployment - Highlights current research outcomes of multiple new technologies in the field of computer vision - Examples and case studies are included
Pushing Frontiers Imaging For Photon Science
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Author : Jiaguo Zhang
language : en
Publisher: Frontiers Media SA
Release Date : 2024-12-24
Pushing Frontiers Imaging For Photon Science written by Jiaguo Zhang and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-24 with Science categories.
Developments of cutting-edge X-ray imaging detectors are largely driven by experiments at the large photon science facilities, i.e. the synchrotron radiation sources and free-electron lasers (FELs) which enable a wealth of investigations in physics, material science, biology, chemistry, environmental sciences, and beyond. The next generation radiation sources, namely diffraction-limited storage-rings (DLSR) and high repetition rate FELs operated in the continuous wave (CW) mode, not only offer brilliant opportunities for research but also pose new challenges and requirements for the X-ray detectors required to exploit them fully. Examples include the high count rate capability required at the DLSRs, the ultra high, continuous frame rate and data throughput at the FELs, and a broad photon energy range from tens of eV to hundreds of keV spanned by the facilities. In order to meet the new requirements posed by the most advanced photon science facilities envisioned or already under development around the world, today various novel photon detection and imaging concepts are being investigated, and detector technologies are advancing fast. The goal of this research topic is to address the challenges and discuss the critical problems encountered in imaging systems for photon science, including but not limited to sensing materials, ASICs, readout electronics, detector systems, and data reduction, Moreover, it will encompass a discussion of the development strategies, technological advances, and recent achievements of each subject - thereby facilitating the realization of complete concepts for novel imaging systems as well as further developments of individual detector technologies.
Artificial Neural Networks And Machine Learning Icann 2021
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Author : Igor Farkaš
language : en
Publisher: Springer Nature
Release Date : 2021-09-10
Artificial Neural Networks And Machine Learning Icann 2021 written by Igor Farkaš and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-10 with Computers categories.
The proceedings set LNCS 12891, LNCS 12892, LNCS 12893, LNCS 12894 and LNCS 12895 constitute the proceedings of the 30th International Conference on Artificial Neural Networks, ICANN 2021, held in Bratislava, Slovakia, in September 2021.* The total of 265 full papers presented in these proceedings was carefully reviewed and selected from 496 submissions, and organized in 5 volumes. In this volume, the papers focus on topics such as model compression, multi-task and multi-label learning, neural network theory, normalization and regularization methods, person re-identification, recurrent neural networks, and reinforcement learning. *The conference was held online 2021 due to the COVID-19 pandemic.
Deep Learning On Edge Computing Devices
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Author : Xichuan Zhou
language : en
Publisher: Elsevier
Release Date : 2022-02-02
Deep Learning On Edge Computing Devices written by Xichuan Zhou and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-02 with Computers categories.
Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture focuses on hardware architecture and embedded deep learning, including neural networks. The title helps researchers maximize the performance of Edge-deep learning models for mobile computing and other applications by presenting neural network algorithms and hardware design optimization approaches for Edge-deep learning. Applications are introduced in each section, and a comprehensive example, smart surveillance cameras, is presented at the end of the book, integrating innovation in both algorithm and hardware architecture. Structured into three parts, the book covers core concepts, theories and algorithms and architecture optimization.This book provides a solution for researchers looking to maximize the performance of deep learning models on Edge-computing devices through algorithm-hardware co-design. - Focuses on hardware architecture and embedded deep learning, including neural networks - Brings together neural network algorithm and hardware design optimization approaches to deep learning, alongside real-world applications - Considers how Edge computing solves privacy, latency and power consumption concerns related to the use of the Cloud - Describes how to maximize the performance of deep learning on Edge-computing devices - Presents the latest research on neural network compression coding, deep learning algorithms, chip co-design and intelligent monitoring
Machine Learning And Knowledge Discovery In Databases
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Author : Frank Hutter
language : en
Publisher: Springer Nature
Release Date : 2021-02-24
Machine Learning And Knowledge Discovery In Databases written by Frank Hutter and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-24 with Computers categories.
The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory;active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.
Machine Learning And Knowledge Discovery In Databases Research Track
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Author : Nuria Oliver
language : en
Publisher: Springer Nature
Release Date : 2021-09-10
Machine Learning And Knowledge Discovery In Databases Research Track written by Nuria Oliver and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-10 with Computers categories.
The multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic. The 210 full papers presented in these proceedings were carefully reviewed and selected from a total of 869 submissions. The volumes are organized in topical sections as follows: Research Track: Part I: Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised and few-shot learning; learning algorithms and applications. Part II: Generative models; algorithms and learning theory; graphs and networks; interpretation, explainability, transparency, safety. Part III: Generative models; search and optimization; supervised learning; text mining and natural language processing; image processing, computer vision and visual analytics. Applied Data Science Track: Part IV: Anomaly detection and malware; spatio-temporal data; e-commerce and finance; healthcare and medical applications (including Covid); mobility and transportation. Part V: Automating machine learning, optimization, and feature engineering; machine learning based simulations and knowledge discovery; recommender systems and behavior modeling; natural language processing; remote sensing, image and video processing; social media.
Intelligence Computation And Applications
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Author : Kangshun Li
language : en
Publisher: Springer Nature
Release Date : 2024-06-30
Intelligence Computation And Applications written by Kangshun Li and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-30 with Computers categories.
This two-volume set, CCIS 2146 and CCIS 2147, constitutes the refereed proceedings of the 14th International Symposium on Intelligence Computation and Applications, ISICA 2023, held in Guangzhou, China, during November 18–19, 2023. The 82 full papers included in these proceedings were carefully reviewed and selected from 178 submissions. The papers presented in these two volumes are organized in the following topical sections: Part I: Frontiers of evolutionary Intelligent Optimization Algorithms; Exploration of computer vision; Machine learning and its applications. Part II: Machine Learning and its applications; Big data analysis and Information security; Intelligent application of computer.
Digital Manufacturing
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Author : Chandrakant D. Patel
language : en
Publisher: Elsevier
Release Date : 2023-12-01
Digital Manufacturing written by Chandrakant D. Patel and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-01 with Technology & Engineering categories.
Digital Manufacturing: Key Elements of a Digital Factory explains the different devices and agents at the factory floor level that are driving the digital manufacturing revolution, including autonomous robots, process automation, artificial intelligence and cyber-physical systems. Individual chapters explore the fundamentals and benefits of major digital manufacturing tools including robotics, the industrial internet of things, digital twins, edge security, knowledge discovery, service-centric production, and related supply-chain strategies. Real-world case studies from industry are provided throughout to show how these work in practice. In addition to learning about individual technologies, readers will discover how they are integrating to drive the digital transformation of manufacturing ecosystem. Final sections present new business models working towards sustainable net zero operations and economy. - Helps produce the "T-shaped" engineers needed in today's digital manufacturing age by providing carefully selected foundational information from a range of disciplines - Includes important coverage of cybersecurity models and analysis - Draws on industry best practice to explain how to implement cutting-edge technologies successfully