The Art of Deep Learning Image Augmentation: The Seeds of Success

Jyotismita Chaki (Autor)

Buch | Softcover

142 Seiten

2025
Springer Nature Switzerland AG (Verlag)
978-981-96-5080-4 (ISBN)

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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.

Dr. Jyotismita Chaki is an Associate Professor at the School of Computer Science and Engineering, Vellore Institute of Technology, India. She holds a Ph.D. in Engineering from Jadavpur University, Kolkata, and her research interests encompass Computer Vision, Image Processing, Pattern Recognition, Medical Imaging, Artificial Intelligence, and Machine Learning. Dr. Chaki is an author and editor, with a substantial body of work including five authored books published by renowned presses like Springer and CRC Press, and six edited books published by CRC Press and Elsevier. She has also published many research articles in high-impact, SCIE-indexed journals, the majority of which are ranked in the top quartiles (Q1 and Q2). In recognition of her contributions, Dr. Chaki was named the world's top 2% scientist by Stanford University and Elsevier in 2024. She is also a Senior Member of the IEEE. Dr. Chaki's editorial contributions are extensive, currently serving as editor for 9 journals, including Engineering Applications of Artificial Intelligence (Elsevier), Scientific Reports (Nature Portfolio), Discover Applied Sciences (Springer Nature), PLOS ONE, PeerJ Computer Science, Computer and Electrical Engineering (Elsevier), Array (Elsevier), Machine Learning with Applications (Elsevier), and BMC Artificial 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.

Erscheinungsdatum	28.05.2025
Reihe/Serie	SpringerBriefs in Applied Sciences and Technology
Reihe/Serie	SpringerBriefs in Computational Intelligence
Zusatzinfo	29 Illustrations, color; 7 Illustrations, black and white
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Informatik ► Grafik / Design ► Digitale Bildverarbeitung
	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Technik ► Elektrotechnik / Energietechnik
Schlagworte	Autoaugment • Autoencoders • Deep learning • Generative Adversarial Network • Image Augmentation
ISBN-10	981-96-5080-1 / 9819650801
ISBN-13	978-981-96-5080-4 / 9789819650804
Zustand	Neuware