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The art of deep learning image augme...

Chaki, Jyotismita.

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The art of deep learning image augmentation = the seeds of success /

Record Type:	Electronic resources : Monograph/item
Title/Author:	The art of deep learning image augmentation/ by Jyotismita Chaki.
Reminder of title:	the seeds of success /
Author:	Chaki, Jyotismita.
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	ix, 142 p. :ill. (chiefly color), digital ;24 cm.
[NT 15003449]:	Chapter 1: Introduction to Deep Learning based Image Augmentation -- Chapter 2: Generative Adversarial Networks (GANs) -- Chapter 3: Autoencoders -- Chapter 4: Applications of Deep Learning Based Image Augmentation -- Chapter 5: Evaluating and Optimizing Deep Learning Image Augmentation Strategies -- Chapter 6: The Future of Deep Learning Image Augmentation.
Contained By:	Springer Nature eBook
Subject:	Image processing - Digital techniques. -
Online resource:	https://doi.org/10.1007/978-981-96-5081-1
ISBN:	9789819650811

The art of deep learning image augmentation = the seeds of success /
Chaki, Jyotismita.

The art of deep learning image augmentationthe seeds of success /[electronic resource] :by Jyotismita Chaki. - Singapore :Springer Nature Singapore :2025. - ix, 142 p. :ill. (chiefly color), digital ;24 cm. - SpringerBriefs in computational intelligence,2625-3712. - SpringerBriefs in computational intelligence..

Chapter 1: Introduction to Deep Learning based Image Augmentation -- Chapter 2: Generative Adversarial Networks (GANs) -- Chapter 3: Autoencoders -- Chapter 4: Applications of Deep Learning Based Image Augmentation -- Chapter 5: Evaluating and Optimizing Deep Learning Image Augmentation Strategies -- Chapter 6: The Future of Deep Learning Image Augmentation.

This book addresses the critical challenge of limited training data in deep learning for computer vision by exploring and evaluating various image augmentation techniques, with a particular emphasis on deep learning-based methods. Chapter 1 establishes the core problem of data scarcity, outlining its negative impacts on model performance, and introduces traditional image augmentation techniques like geometric transformations, color space manipulations, and other methods such as noise injection. It highlights the limitations of these traditional approaches, including limited variation, lack of control, and inability to introduce new information, before introducing the advantages of deep learning-based augmentation, such as superior control, task adaptability, enhanced realism, and automation. Chapter 2 delves into GAN-based image augmentation, discussing how GANs generate realistic synthetic images for various applications like super-resolution and image-to-image translation, while also addressing the challenges associated with GAN training and potential future directions. Chapter 3 explores autoencoder-based image augmentation, covering techniques like VAEs, DAEs, and AAEs, and highlighting architectural considerations and challenges such as overfitting. Chapter 4 showcases the diverse applications of deep learning-based image augmentation and how it enhances various computer vision tasks by improving generalization, robustness, and accuracy. Chapter 5 discusses strategies for evaluating and optimizing deep learning image augmentation, including traditional metrics, image quality metrics, and hyperparameter tuning techniques. Finally, Chapter 6 explores cutting-edge advancements, covering AutoAugment, interpretable augmentation, attention-based augmentation, counterfactual augmentation, and human-in-the-loop augmentation, emphasizing the role of human expertise in creating high-quality augmented data.

ISBN: 9789819650811

Standard No.: 10.1007/978-981-96-5081-1doiSubjects--Topical Terms:

532550
Image processing
--Digital techniques.

LC Class. No.: TA1637

Dewey Class. No.: 621.367

The art of deep learning image augmentation = the seeds of success /
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505 0 $a Chapter 1: Introduction to Deep Learning based Image Augmentation -- Chapter 2: Generative Adversarial Networks (GANs) -- Chapter 3: Autoencoders -- Chapter 4: Applications of Deep Learning Based Image Augmentation -- Chapter 5: Evaluating and Optimizing Deep Learning Image Augmentation Strategies -- Chapter 6: The Future of Deep Learning Image Augmentation.
520 $a This book addresses the critical challenge of limited training data in deep learning for computer vision by exploring and evaluating various image augmentation techniques, with a particular emphasis on deep learning-based methods. Chapter 1 establishes the core problem of data scarcity, outlining its negative impacts on model performance, and introduces traditional image augmentation techniques like geometric transformations, color space manipulations, and other methods such as noise injection. It highlights the limitations of these traditional approaches, including limited variation, lack of control, and inability to introduce new information, before introducing the advantages of deep learning-based augmentation, such as superior control, task adaptability, enhanced realism, and automation. Chapter 2 delves into GAN-based image augmentation, discussing how GANs generate realistic synthetic images for various applications like super-resolution and image-to-image translation, while also addressing the challenges associated with GAN training and potential future directions. Chapter 3 explores autoencoder-based image augmentation, covering techniques like VAEs, DAEs, and AAEs, and highlighting architectural considerations and challenges such as overfitting. Chapter 4 showcases the diverse applications of deep learning-based image augmentation and how it enhances various computer vision tasks by improving generalization, robustness, and accuracy. Chapter 5 discusses strategies for evaluating and optimizing deep learning image augmentation, including traditional metrics, image quality metrics, and hyperparameter tuning techniques. Finally, Chapter 6 explores cutting-edge advancements, covering AutoAugment, interpretable augmentation, attention-based augmentation, counterfactual augmentation, and human-in-the-loop augmentation, emphasizing the role of human expertise in creating high-quality augmented data.
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