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 ...
Read More
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.
Read Less
Add this copy of The Art of Deep Learning Image Augmentation: The Seeds to cart. $60.65, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2025 by Springer Nature Switzerland AG.
Choose your shipping method in Checkout. Costs may vary based on destination.
Seller's Description:
New. Print on demand Contains: Illustrations, black & white, Illustrations, color. SpringerBriefs in Computational Intelligence ; SpringerBriefs in Applied Sciences and Technology . IX, 142 p. 36 illus., 29 illus. in color. Intended for professional and scholarly audience.
Add this copy of The Art of Deep Learning Image Augmentation: the Seeds to cart. $70.13, new condition, Sold by Just one more Chapter rated 3.0 out of 5 stars, ships from Miramar, FL, UNITED STATES, published 2025 by Springer.
