Synthetic data for deep learning:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham, Switzerland
Springer
[2021]
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| Schriftenreihe: | Springer optimization and its applications
volume 174 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xii, 348 Seiten Illustrationen, Diagramme (überwiegend farbig) |
| ISBN: | 9783030751777 |
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Datensatz im Suchindex
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| adam_text |
Contents 1 Introduction: The Data Problem. 1.1 Are Machine Learning Models Hitting a Wall?. 1.2 One-Shot Learning and Beyond: Less Data for More Classes. 4 1.3 Weakly Supervised Training: Trading Labels for Computation. 7 1.4 Machine Learning Without Data: Leaving Moore’s Law in the Dust. 8 1.5 Why Synthetic Data?. 1.6 The Plan . 11 13 1 1 2 Deep 2.1 2.2 2.3 2.4 2.5 2.6 Learning and Optimization. The Deep Learning Revolution. A (Very) Brief Introduction to Machine Learning. Introduction to Deep Learning. First-Order Optimization in DeepLearning. Adaptive Gradient Descent Algorithms. Conclusion. 19 19 22 30 40 47 57 3 Deep 3.1 3.2 3.3 3.4 3.5 Neural Networks for Computer Vision . Computer Vision and Convolutional Neural Networks.
Modern Convolutional Architectures. Case Study: Neural Architectures for Object Detection. Data Augmentations: The First Step to Synthetic Data. Conclusion. 59 59 66 76 88 95 4 Generative Models in Deep Learning. 4.1 Introduction to Generative Models. 4.2 Taxonomy of Generative Models in Deep Learning and Tractable Density Models: FVBNs and Normalizing Flows. 102 97 97 vii
viii Contents 4.3 4.4 4.5 4.6 4.7 4.8 Approximate Explicit Density Models: VAE. Generative Adversarial Networks. Loss Functions in GANs. GAN-Based Architectures. Case Study: GAN-Based Style Transfer. Conclusion. 108 113 117 121 125 136 5 The 5.1 5.2 5.3 5.4 6 Synthetic Data for Basic Computer Vision Problems. 6.1 Introduction. 6.2 Low-Level Computer Vision. 6.3 Datasets of Basic Objects. 6.4 Case Study: Object Detection With Synthetic Data. 6.5 Other High-Level Computer Vision Problems. 6.6 Synthetic People. 6.7 Other Vision-Related Tasks: OCR and Visual Reasoning. 6.8 Conclusion. 161 161 163 166 171 181 184 190 194 7 Synthetic Simulated Environments. 7.1
Introduction. 7.2 Urban and Outdoor Environments: Learning to Drive. 7.3 Datasets and Simulators of Indoor Scenes. 7.4 Robotic Simulators. 7.5 Vision-Based Applications in Unmanned Aerial Vehicles. 7.6 Computer Games as Virtual Environments. Ί.Ί Conclusion. 195 195 197 205 208 211 214 215 8 Synthetic Data Outside Computer Vision. 217 8.1 Synthetic System Logs for Fraud and Intrusion Detection. 217 8.2 Synthetic Data for Neural Programming. 220 8.3 Synthetic Data in Bioinformatics . 222 8.4 Synthetic Data in Natural Language Processing. 224 8.5 Conclusion. 226 9 Directions in Synthetic Data Development. 227 9.1 Domain Randomization. 227 9.2 Improving CGI-Based Generation. 229 Early Days of Synthetic Data. 139 Line Drawings: The First Steps of
Computer Vision. 139 Synthetic Data as a Testbed for Quantitative Comparisons . 142 ALVINN: A Self-Driving Neural Network in 1989. 145 Early Simulation Environments: Robots and the Critique of Simulation. 149 5.5 Case Study: MOBOT and The Problems of Simulation. 154 5.6 Conclusion. 159
Contents ix Compositing Real Data to Produce Synthetic Datasets . Synthetic Data Produced by Generative Models. 230 233 Synthetic-to-Real Domain Adaptation and Refinement. 10.1 Synthetic-to-Real Domain Adaptation and Refinement. 10.2 Case Study: GAN-Based Refinement for Gaze Estimation. 10.3 Refining Synthetic Data with GANs. 10.4 Making Synthetic Data from Real with GANs. 10.5 Domain Adaptation at the Feature/Model Level. 10.6 Domain Adaptation for Control and Robotics. 10.7 Case Study: GAN-Based Domain Adaptation for Medical Imaging. 261 10.8 Conclusion. 235 235 236 240 245 252 257 Privacy Guarantees in Synthetic Data. 11.1 Why is Privacy Important?. 11.2 Introduction to Differential Privacy. 11.3 Differential Privacy in Deep Learning. 11.4 Differential Privacy Guarantees for Synthetic Data Generation. 277 11.5 Case Study: Synthetic Data in Economics, Healthcare, and Social
Sciences. 280 11.6 Conclusion. 269 269 272 274 Promising Directions for Future Work. 12.1 Procedural Generation of Synthetic Data. 12.2 From Domain Randomization to the Generation Feedback Loop. 12.3 Improving Domain Adaptation with Domain Knowledge. 12.4 Additional Modalities for Domain Adaptation Architectures . 12.5 Conclusion. 285 285 287 290 291 293 References. 295 9.3 9.4 10 11 12 267 283 |
| adam_txt |
Contents 1 Introduction: The Data Problem. 1.1 Are Machine Learning Models Hitting a Wall?. 1.2 One-Shot Learning and Beyond: Less Data for More Classes. 4 1.3 Weakly Supervised Training: Trading Labels for Computation. 7 1.4 Machine Learning Without Data: Leaving Moore’s Law in the Dust. 8 1.5 Why Synthetic Data?. 1.6 The Plan . 11 13 1 1 2 Deep 2.1 2.2 2.3 2.4 2.5 2.6 Learning and Optimization. The Deep Learning Revolution. A (Very) Brief Introduction to Machine Learning. Introduction to Deep Learning. First-Order Optimization in DeepLearning. Adaptive Gradient Descent Algorithms. Conclusion. 19 19 22 30 40 47 57 3 Deep 3.1 3.2 3.3 3.4 3.5 Neural Networks for Computer Vision . Computer Vision and Convolutional Neural Networks.
Modern Convolutional Architectures. Case Study: Neural Architectures for Object Detection. Data Augmentations: The First Step to Synthetic Data. Conclusion. 59 59 66 76 88 95 4 Generative Models in Deep Learning. 4.1 Introduction to Generative Models. 4.2 Taxonomy of Generative Models in Deep Learning and Tractable Density Models: FVBNs and Normalizing Flows. 102 97 97 vii
viii Contents 4.3 4.4 4.5 4.6 4.7 4.8 Approximate Explicit Density Models: VAE. Generative Adversarial Networks. Loss Functions in GANs. GAN-Based Architectures. Case Study: GAN-Based Style Transfer. Conclusion. 108 113 117 121 125 136 5 The 5.1 5.2 5.3 5.4 6 Synthetic Data for Basic Computer Vision Problems. 6.1 Introduction. 6.2 Low-Level Computer Vision. 6.3 Datasets of Basic Objects. 6.4 Case Study: Object Detection With Synthetic Data. 6.5 Other High-Level Computer Vision Problems. 6.6 Synthetic People. 6.7 Other Vision-Related Tasks: OCR and Visual Reasoning. 6.8 Conclusion. 161 161 163 166 171 181 184 190 194 7 Synthetic Simulated Environments. 7.1
Introduction. 7.2 Urban and Outdoor Environments: Learning to Drive. 7.3 Datasets and Simulators of Indoor Scenes. 7.4 Robotic Simulators. 7.5 Vision-Based Applications in Unmanned Aerial Vehicles. 7.6 Computer Games as Virtual Environments. Ί.Ί Conclusion. 195 195 197 205 208 211 214 215 8 Synthetic Data Outside Computer Vision. 217 8.1 Synthetic System Logs for Fraud and Intrusion Detection. 217 8.2 Synthetic Data for Neural Programming. 220 8.3 Synthetic Data in Bioinformatics . 222 8.4 Synthetic Data in Natural Language Processing. 224 8.5 Conclusion. 226 9 Directions in Synthetic Data Development. 227 9.1 Domain Randomization. 227 9.2 Improving CGI-Based Generation. 229 Early Days of Synthetic Data. 139 Line Drawings: The First Steps of
Computer Vision. 139 Synthetic Data as a Testbed for Quantitative Comparisons . 142 ALVINN: A Self-Driving Neural Network in 1989. 145 Early Simulation Environments: Robots and the Critique of Simulation. 149 5.5 Case Study: MOBOT and The Problems of Simulation. 154 5.6 Conclusion. 159
Contents ix Compositing Real Data to Produce Synthetic Datasets . Synthetic Data Produced by Generative Models. 230 233 Synthetic-to-Real Domain Adaptation and Refinement. 10.1 Synthetic-to-Real Domain Adaptation and Refinement. 10.2 Case Study: GAN-Based Refinement for Gaze Estimation. 10.3 Refining Synthetic Data with GANs. 10.4 Making Synthetic Data from Real with GANs. 10.5 Domain Adaptation at the Feature/Model Level. 10.6 Domain Adaptation for Control and Robotics. 10.7 Case Study: GAN-Based Domain Adaptation for Medical Imaging. 261 10.8 Conclusion. 235 235 236 240 245 252 257 Privacy Guarantees in Synthetic Data. 11.1 Why is Privacy Important?. 11.2 Introduction to Differential Privacy. 11.3 Differential Privacy in Deep Learning. 11.4 Differential Privacy Guarantees for Synthetic Data Generation. 277 11.5 Case Study: Synthetic Data in Economics, Healthcare, and Social
Sciences. 280 11.6 Conclusion. 269 269 272 274 Promising Directions for Future Work. 12.1 Procedural Generation of Synthetic Data. 12.2 From Domain Randomization to the Generation Feedback Loop. 12.3 Improving Domain Adaptation with Domain Knowledge. 12.4 Additional Modalities for Domain Adaptation Architectures . 12.5 Conclusion. 285 285 287 290 291 293 References. 295 9.3 9.4 10 11 12 267 283 |
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| index_date | 2024-07-03T18:48:40Z |
| indexdate | 2025-02-13T09:00:45Z |
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| isbn | 9783030751777 |
| language | English |
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| spelling | Nikolenko, Sergey 1984- Verfasser (DE-588)128240038X aut Synthetic data for deep learning Sergey I. Nikolenko Cham, Switzerland Springer [2021] © 2021 xii, 348 Seiten Illustrationen, Diagramme (überwiegend farbig) txt rdacontent n rdamedia nc rdacarrier Springer optimization and its applications volume 174 Machine Learning Operations Research, Management Science Image Processing and Computer Vision Machine learning Operations research Management science Optical data processing Deep Learning (DE-588)1135597375 gnd rswk-swf Big Data (DE-588)4802620-7 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Data Science (DE-588)1140936166 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Data Science (DE-588)1140936166 s Big Data (DE-588)4802620-7 s Neuronales Netz (DE-588)4226127-2 s Maschinelles Lernen (DE-588)4193754-5 s Deep Learning (DE-588)1135597375 s DE-604 Erscheint auch als Online-Ausgabe 978-3-030-75178-4 Springer optimization and its applications volume 174 (DE-604)BV021746093 174 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033029651&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Nikolenko, Sergey 1984- Synthetic data for deep learning Springer optimization and its applications Machine Learning Operations Research, Management Science Image Processing and Computer Vision Machine learning Operations research Management science Optical data processing Deep Learning (DE-588)1135597375 gnd Big Data (DE-588)4802620-7 gnd Neuronales Netz (DE-588)4226127-2 gnd Data Science (DE-588)1140936166 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)1135597375 (DE-588)4802620-7 (DE-588)4226127-2 (DE-588)1140936166 (DE-588)4193754-5 |
| title | Synthetic data for deep learning |
| title_auth | Synthetic data for deep learning |
| title_exact_search | Synthetic data for deep learning |
| title_exact_search_txtP | Synthetic data for deep learning |
| title_full | Synthetic data for deep learning Sergey I. Nikolenko |
| title_fullStr | Synthetic data for deep learning Sergey I. Nikolenko |
| title_full_unstemmed | Synthetic data for deep learning Sergey I. Nikolenko |
| title_short | Synthetic data for deep learning |
| title_sort | synthetic data for deep learning |
| topic | Machine Learning Operations Research, Management Science Image Processing and Computer Vision Machine learning Operations research Management science Optical data processing Deep Learning (DE-588)1135597375 gnd Big Data (DE-588)4802620-7 gnd Neuronales Netz (DE-588)4226127-2 gnd Data Science (DE-588)1140936166 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Machine Learning Operations Research, Management Science Image Processing and Computer Vision Machine learning Operations research Management science Optical data processing Deep Learning Big Data Neuronales Netz Data Science Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033029651&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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