LEADER 00873nam0-22002771i-450 001 990001715730403321 005 20170518112519.0 035 $a000171573 035 $aFED01000171573 035 $a(Aleph)000171573FED01 035 $a000171573 100 $a20030910d1874----km-y0itay50------ba 101 0 $aita 200 1 $aManuale di chimica applicata alle arti e all' industria redatto... per gli Istituti tecnici$fAntonio Selmi. 210 $aMilano$cGalli e Omedei$d1874 215 $a571 p.$cill$d19 cm 610 0 $aChimica applicata 676 $a660 700 1$aSelmi,$bAntonio$068317 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990001715730403321 952 $a60 660 C 2$b10077$fFAGBC 959 $aFAGBC 996 $aManuale di chimica applicata alle arti e all' industria redatto... per gli Istituti tecnici$9359026 997 $aUNINA LEADER 04161nam 22005655 450 001 9911001456303321 005 20250502130157.0 010 $a981-9650-81-X 024 7 $a10.1007/978-981-96-5081-1 035 $a(CKB)38753630900041 035 $a(DE-He213)978-981-96-5081-1 035 $a(MiAaPQ)EBC32076190 035 $a(Au-PeEL)EBL32076190 035 $a(EXLCZ)9938753630900041 100 $a20250502d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 14$aThe Art of Deep Learning Image Augmentation: The Seeds of Success /$fby Jyotismita Chaki 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (IX, 142 p. 36 illus., 29 illus. in color.) 225 1 $aSpringerBriefs in Computational Intelligence,$x2625-3712 311 08$a981-9650-80-1 327 $aChapter 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. 330 $aThis book addresses the critical challenge of limited training data in deep learning for computer vision by exploring and evaluating various image augmentation techniques, with a particular emphasis on deep learning-based methods. Chapter 1 establishes the core problem of data scarcity, outlining its negative impacts on model performance, and introduces traditional image augmentation techniques like geometric transformations, color space manipulations, and other methods such as noise injection. It highlights the limitations of these traditional approaches, including limited variation, lack of control, and inability to introduce new information, before introducing the advantages of deep learning-based augmentation, such as superior control, task adaptability, enhanced realism, and automation. Chapter 2 delves into GAN-based image augmentation, discussing how GANs generate realistic synthetic images for various applications like super-resolution and image-to-image translation, while also addressing the challenges associated with GAN training and potential future directions. Chapter 3 explores autoencoder-based image augmentation, covering techniques like VAEs, DAEs, and AAEs, and highlighting architectural considerations and challenges such as overfitting. Chapter 4 showcases the diverse applications of deep learning-based image augmentation and how it enhances various computer vision tasks by improving generalization, robustness, and accuracy. Chapter 5 discusses strategies for evaluating and optimizing deep learning image augmentation, including traditional metrics, image quality metrics, and hyperparameter tuning techniques. Finally, Chapter 6 explores cutting-edge advancements, covering AutoAugment, interpretable augmentation, attention-based augmentation, counterfactual augmentation, and human-in-the-loop augmentation, emphasizing the role of human expertise in creating high-quality augmented data. 410 0$aSpringerBriefs in Computational Intelligence,$x2625-3712 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aImage processing 606 $aComputational Intelligence 606 $aArtificial Intelligence 606 $aImage Processing 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aImage processing. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aImage Processing. 676 $a006.3 700 $aChaki$b Jyotismita$4aut$4http://id.loc.gov/vocabulary/relators/aut$01226856 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911001456303321 996 $aThe Art of Deep Learning Image Augmentation: The Seeds of Success$94384565 997 $aUNINA