Deep Active Learning
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Deep Active Learning
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Author : Kayo Matsushita
language : en
Publisher: Springer
Release Date : 2017-09-12
Deep Active Learning written by Kayo Matsushita and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-12 with Education categories.
This is the first book to connect the concepts of active learning and deep learning, and to delineate theory and practice through collaboration between scholars in higher education from three countries (Japan, the United States, and Sweden) as well as different subject areas (education, psychology, learning science, teacher training, dentistry, and business).It is only since the beginning of the twenty-first century that active learning has become key to the shift from teaching to learning in Japanese higher education. However, “active learning” in Japan, as in many other countries, is just an umbrella term for teaching methods that promote students’ active participation, such as group work, discussions, presentations, and so on.What is needed for students is not just active learning but deep active learning. Deep learning focuses on content and quality of learning whereas active learning, especially in Japan, focuses on methods of learning. Deep active learning is placed at the intersection of active learning and deep learning, referring to learning that engages students with the world as an object of learning while interacting with others, and helps the students connect what they are learning with their previous knowledge and experiences as well as their future lives.What curricula, pedagogies, assessments and learning environments facilitate such deep active learning? This book attempts to respond to that question by linking theory with practice.
The Impact Of Active Learning On Deep Learning Models With Detectron 2
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Author : Daan De Wilde
language : en
Publisher:
Release Date : 2023
The Impact Of Active Learning On Deep Learning Models With Detectron 2 written by Daan De Wilde 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.
Advances In Deep Active Learning And Synergies With Semi Supervision
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Author : Sandra Gilhuber
language : en
Publisher:
Release Date : 2025
Advances In Deep Active Learning And Synergies With Semi Supervision written by Sandra Gilhuber and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025 with categories.
Accelerating The Training Of Convolutional Neural Networks For Image Segmentation With Deep Active Learning
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Author : Wei Tao Chen
language : en
Publisher:
Release Date : 2020
Accelerating The Training Of Convolutional Neural Networks For Image Segmentation With Deep Active Learning written by Wei Tao Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Image semantic segmentation is an important problem in computer vision. However, Training a deep neural network for semantic segmentation in supervised learning requires expensive manual labeling. Active learning (AL) addresses this problem by automatically selecting a subset of the dataset to label and iteratively improve the model. This minimizes labeling costs while maximizing performance. Yet, deep active learning for image segmentation has not been systematically studied in the literature. This thesis offers three contributions. First, we compare six different state-of-the-art querying methods, including uncertainty, Bayesian, and out-of-distribution methods, in the context of active learning for image segmentation. The comparison uses the standard dataset Cityscapes, as well as randomly generated data, and the state-of-the-art image segmentation architecture DeepLab. Our results demonstrate subtle but robust differences between the querying methods, which we analyze and explain. Second, we propose a novel way to query images by counting the number of pixels with acquisition values above a certain threshold. Our counting method outperforms the standard averaging method. Lastly, we demonstrate that the previous two findings remain consistent for both whole images and image crops. Furthermore, we provide an in-depth discussion of deep active learning and results from supplementary experiments. First, we studied active learning in the context of image classification with the MNIST dataset. We observed an interesting phenomenon where active learning querying methods perform worse than random sampling in the early cycles but overtake random sampling at a break-even point. This break-even point can be controlled by varying model capacity, sample diversity, and temperature scaling. The difference in performances of the six querying methods is larger than in the case of image segmentation. Second, we attempt to explore the theoretical optimal query by querying samples with the lowest accuracy and querying with a trained expert model. Although they turned out to be suboptimal, their results would hopefully shed light on the subject. Lastly, we present the experiment results from using SegNet and FCN. With these architectures, our querying methods did not perform any better than random sampling. Nevertheless, those negative results demonstrate some of the difficulties of active learning for image segmentation.
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
The Digital University
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Author : Reza Hazemi
language : en
Publisher: Springer
Release Date : 1998-08-12
The Digital University written by Reza Hazemi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-08-12 with Computers categories.
Examining the rapidly growing field of remote computer-based learning, this title discusses how to use and create a Web-based system for teaching and learning, using groupware for collaboration, multimedia, distance learning, and much more.
Fourth International Software Metrics Symposium
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Author :
language : en
Publisher: Institute of Electrical & Electronics Engineers(IEEE)
Release Date : 1997
Fourth International Software Metrics Symposium written by and has been published by Institute of Electrical & Electronics Engineers(IEEE) this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Computers categories.
This volume on software design and development covers the proceedings of the 4th International Software Metrics Symposium held in 1997."
Active Learning
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Author : Burr Settles
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Active Learning written by Burr Settles and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose "queries," usually in the form of unlabeled data instances to be labeled by an "oracle" (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain. This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or "query selection frameworks." We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities. Table of Contents: Automating Inquiry / Uncertainty Sampling / Searching Through the Hypothesis Space / Minimizing Expected Error and Variance / Exploiting Structure in Data / Theory / Practical Considerations
Interactive Machine Learning From Theory To Scale
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Author : Yinglun Zhu
language : en
Publisher:
Release Date : 2023
Interactive Machine Learning From Theory To Scale written by Yinglun Zhu 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.
While machine learning has made unprecedented successes in many real-world scenarios, most learning approaches require a huge amount of training data. Such a requirement imposes real challenges to the practitioners, e.g., data annotation can be expensive and time-consuming. To overcome these challenges, this dissertation studies interactive machine learning, where learning is conducted in a closed-loop manner: The learner uses previously collected information to guide future decisions (e.g., which data points to label next), which in turn help make the following predictions. This dissertation focuses on developing novel algorithmic principles and uncovering fundamental limits when scaling interactive machine learning into real-world settings at large scales. More specifically, we study interactive machine learning with (i) noisy data and rich model classes, (ii) large action spaces, and (iii) model selection requirements; this dissertation is thus grouped into three corresponding parts. To bring the promise of interactive learning into the real world, we develop novel human-in-the-loop learning algorithms and systems that achieve both statistical efficiency and computational efficiency. In the first part, we study active machine learning with noisy data and rich model classes. While huge successes of active learning have been observed, due to technical difficulties, most guarantees are developed (i) under low-noise assumptions, and (ii) for simple model classes. We develop efficient algorithms that bypass these two fundamental barriers and thus make an essential step toward real-world applications of active learning. More specifically, by leveraging the power of abstention, we develop the first efficient, general-purpose active learning algorithm that achieves exponential label savings without any low-noise assumptions. We also develop the first deep active learning (i.e., active learning with neural networks) algorithms that achieve exponential label savings when equipped with an abstention option. In the second part, we study decision making with large action spaces. While researchers have explored decision making when the number of alternatives (e.g., actions) is small, guarantees for decision making in large, continuous action spaces remained elusive, leading to a significant gap between theory and practice. In this part, we bridge this gap by developing the first efficient, general-purpose contextual bandits algorithms for large action spaces, in both structured and unstructured cases. Our algorithms make use of standard computational oracles, and achieve nearly optimal guarantees, and have runtime and memory independent of the size of the action space. Our algorithms are also highly practical: They achieve the state-of-the-art performance on an Amazon dataset with nearly 3 million categories. In the third part, we study model selection in decision making. Model selection is the fundamental task in supervised learning, but it faces unique challenges when deployed in decision making: Decisions are made online and only partial feedback is observed. Focusing on the regret minimization setting, we establish fundamental lower bounds showing that model selection in decision making is strictly harder than model selection in standard supervised learning: Compared to an additional logarithmic cost suffered in supervised learning, one has to pay an additional polynomial cost in decision making. Nevertheless, we develop Pareto optimal algorithms that achieve matching guarantees (up to logarithmic factors). Focusing on the best action identification setting, we develop novel algorithms and show that model selection in best action identification can be achieved without too much additional cost.
Deep Active Learning For Object Detection In Robocup Soccer
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Author : Jonas Hagge
language : en
Publisher:
Release Date : 2021
Deep Active Learning For Object Detection In Robocup Soccer written by Jonas Hagge and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.