Principles of Catastrophic Forgetting for Continual Semantic Segmentation in Automated Driving PDF Download
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Author: Kalb, Tobias Michael
Publisher: KIT Scientific Publishing
ISBN: 3731513730
Category :
Languages : en
Pages : 236
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Book Description
Deep learning excels at extracting complex patterns but faces catastrophic forgetting when fine-tuned on new data. This book investigates how class- and domain-incremental learning affect neural networks for automated driving, identifying semantic shifts and feature changes as key factors. Tools for quantitatively measuring forgetting are selected and used to show how strategies like image augmentation, pretraining, and architectural adaptations mitigate catastrophic forgetting.
Author: Kalb, Tobias Michael
Publisher: KIT Scientific Publishing
ISBN: 3731513730
Category :
Languages : en
Pages : 236
Get Book Here
Book Description
Deep learning excels at extracting complex patterns but faces catastrophic forgetting when fine-tuned on new data. This book investigates how class- and domain-incremental learning affect neural networks for automated driving, identifying semantic shifts and feature changes as key factors. Tools for quantitatively measuring forgetting are selected and used to show how strategies like image augmentation, pretraining, and architectural adaptations mitigate catastrophic forgetting.
Author: Gail A. Carpenter
Publisher: MIT Press
ISBN: 9780262031769
Category : Computers
Languages : en
Pages : 724
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Book Description
Pattern Recognition by Self-Organizing Neural Networks presentsthe most recent advances in an area of research that is becoming vitally important in the fields ofcognitive science, neuroscience, artificial intelligence, and neural networks in general. The 19articles take up developments in competitive learning and computational maps, adaptive resonancetheory, and specialized architectures and biological connections. Introductorysurvey articles provide a framework for understanding the many models involved in various approachesto studying neural networks. These are followed in Part 2 by articles that form the foundation formodels of competitive learning and computational mapping, and recent articles by Kohonen, applyingthem to problems in speech recognition, and by Hecht-Nielsen, applying them to problems in designingadaptive lookup tables. Articles in Part 3 focus on adaptive resonance theory (ART) networks,selforganizing pattern recognition systems whose top-down template feedback signals guarantee theirstable learning in response to arbitrary sequences of input patterns. In Part 4, articles describeembedding ART modules into larger architectures and provide experimental evidence fromneurophysiology, event-related potentials, and psychology that support the prediction that ARTmechanisms exist in the brain. Contributors: J.-P. Banquet, G.A. Carpenter, S.Grossberg, R. Hecht-Nielsen, T. Kohonen, B. Kosko, T.W. Ryan, N.A. Schmajuk, W. Singer, D. Stork, C.von der Malsburg, C.L. Winter.
Author: E. R. Davies
Publisher: Academic Press
ISBN: 0128221496
Category : Technology & Engineering
Languages : en
Pages : 584
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Book Description
Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine and deep learning techniques that have emerged during the past 5–10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, image inpainting, novelty and anomaly detection. This book provides easy learning for researchers and practitioners of advanced computer vision methods, but it is also suitable as a textbook for a second course on computer vision and deep learning for advanced undergraduates and graduate students. - Provides an important reference on deep learning and advanced computer methods that was created by leaders in the field - Illustrates principles with modern, real-world applications - Suitable for self-learning or as a text for graduate courses
Author: Diego Pérez Liébana
Publisher: Morgan & Claypool Publishers
ISBN: 1681736454
Category : Computers
Languages : en
Pages : 193
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Book Description
Research on general video game playing aims at designing agents or content generators that can perform well in multiple video games, possibly without knowing the game in advance and with little to no specific domain knowledge. The general video game AI framework and competition propose a challenge in which researchers can test their favorite AI methods with a potentially infinite number of games created using the Video Game Description Language. The open-source framework has been used since 2014 for running a challenge. Competitors around the globe submit their best approaches that aim to generalize well across games. Additionally, the framework has been used in AI modules by many higher-education institutions as assignments, or as proposed projects for final year (undergraduate and Master's) students and Ph.D. candidates. The present book, written by the developers and organizers of the framework, presents the most interesting highlights of the research performed by the authors during these years in this domain. It showcases work on methods to play the games, generators of content, and video game optimization. It also outlines potential further work in an area that offers multiple research directions for the future.
Author: Leslie Pack Kaelbling
Publisher: MIT Press
ISBN: 9780262111744
Category : Computers
Languages : en
Pages : 206
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Book Description
Learning to perform complex action strategies is an important problem in the fields of artificial intelligence, robotics and machine learning. Presenting interesting, new experimental results, Learning in Embedded Systems explores algorithms that learn efficiently from trial and error experience with an external world. The text is a detailed exploration of the problem of learning action strategies in the context of designing embedded systems that adapt their behaviour to a complex, changing environment. Such systems include mobile robots, factory process controllers and long-term software databases.
Author: Suvrit Sra
Publisher: MIT Press
ISBN: 026201646X
Category : Computers
Languages : en
Pages : 509
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Book Description
An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.
Author: IEEE Staff
Publisher:
ISBN: 9781665458252
Category :
Languages : en
Pages :
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Book Description
Recent efforts in computer vision have demonstrated impressive successes on a variety of realworld challenges WACV conferences provide a forum for computer vision researchers working on practical applications to share their latest developments WACV 2019 solicits high quality, original submissions describing research on computer vision applications Unlike other vision conferences, WACV emphasizes papers on systems and applications with significant, interesting vision components Authors are encouraged to submit applications papers, as well as more traditional algorithms papers
Author: Terrence W. Deacon
Publisher: W. W. Norton & Company
ISBN: 0393343022
Category : Science
Languages : en
Pages : 532
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Book Description
"A work of enormous breadth, likely to pleasantly surprise both general readers and experts."—New York Times Book Review This revolutionary book provides fresh answers to long-standing questions of human origins and consciousness. Drawing on his breakthrough research in comparative neuroscience, Terrence Deacon offers a wealth of insights into the significance of symbolic thinking: from the co-evolutionary exchange between language and brains over two million years of hominid evolution to the ethical repercussions that followed man's newfound access to other people's thoughts and emotions. Informing these insights is a new understanding of how Darwinian processes underlie the brain's development and function as well as its evolution. In contrast to much contemporary neuroscience that treats the brain as no more or less than a computer, Deacon provides a new clarity of vision into the mechanism of mind. It injects a renewed sense of adventure into the experience of being human.
Author: Jean-Pierre Dal Pont
Publisher: John Wiley & Sons
ISBN: 1118565983
Category : Technology & Engineering
Languages : en
Pages : 382
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Book Description
Process Engineering, the science and art of transforming raw materials and energy into a vast array of commercial materials, was conceived at the end of the 19th Century. Its history in the role of the Process Industries has been quite honorable, and techniques and products have contributed to improve health, welfare and quality of life. Today, industrial enterprises, which are still a major source of wealth, have to deal with new challenges in a global world. They need to reconsider their strategy taking into account environmental constraints, social requirements, profit, competition, and resource depletion. “Systems thinking” is a prerequisite from process development at the lab level to good project management. New manufacturing concepts have to be considered, taking into account LCA, supply chain management, recycling, plant flexibility, continuous development, process intensification and innovation. This book combines experience from academia and industry in the field of industrialization, i.e. in all processes involved in the conversion of research into successful operations. Enterprises are facing major challenges in a world of fierce competition and globalization. Process engineering techniques provide Process Industries with the necessary tools to cope with these issues. The chapters of this book give a new approach to the management of technology, projects and manufacturing. Contents Part 1: The Company as of Today 1. The Industrial Company: its Purpose, History, Context, and its Tomorrow?, Jean-Pierre Dal Pont. 2. The Two Modes of Operation of the Company – Operational and Entrepreneurial, Jean-Pierre Dal Pont. 3. The Strategic Management of the Company: Industrial Aspects, Jean-Pierre Dal Pont. Part 2: Process Development and Industrialization 4. Chemical Engineering and Process Engineering, Jean-Pierre Dal Pont. 5. Foundations of Process Industrialization, Jean-François Joly. 6. The Industrialization Process: Preliminary Projects, Jean-Pierre Dal Pont and Michel Royer. 7. Lifecycle Analysis and Eco-Design: Innovation Tools for Sustainable Industrial Chemistry, Sylvain Caillol. 8. Methods for Design and Evaluation of Sustainable Processes and Industrial Systems, Catherine Azzaro-Pantel. 9. Project Management Techniques: Engineering, Jean-Pierre Dal Pont. Part 3: The Necessary Adaptation of the Company for the Future 10. Japanese Methods, Jean-Pierre Dal Pont. 11. Innovation in Chemical Engineering Industries, Oliver Potier and Mauricio Camargo. 12. The Place of Intensified Processes in the Plant of the Future, Laurent Falk. 13. Change Management, Jean-Pierre Dal Pont. 14. The Plant of the Future, Jean-Pierre Dal Pont.
Author: Alex Graves
Publisher: Springer
ISBN: 3642247970
Category : Technology & Engineering
Languages : en
Pages : 148
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Book Description
Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools—robust to input noise and distortion, able to exploit long-range contextual information—that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary. The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video. Experimental validation is provided by state-of-the-art results in speech and handwriting recognition.
