Deep Learning With Very Few Training Examples

Christoph Reinders

doi:10.51202/9783186889102

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Bibliographic data

Publication year: 2025
Publication date: 13.03.2025
ISBN-Print: 978-3-18-388910-5
ISBN-Online: 978-3-18-688910-2
Publisher: VDI Verlag, Düsseldorf
Series: Informatik/ Kommunikation
Volume: 889
Language: German
Pages: 121
Product type: Book Titles

Titelei/Inhaltsverzeichnis No access Pages I - XII
1. Deep Learning No access
2. SmallData No access
3. 1. Part I: Small Data Object Detection No access
  2. Part II: Neural Random Forest Imitation No access
  3. Part III: ChimeraMix & HydraMix No access
4. List of Publications No access
5. Outline No access
1. Object Detection No access
2. Random Forest to Neural Network Transformation No access
3. 1. Regularization No access
  2. Data Augmentation No access
  3. MixingAugmentation No access
1. ArtificialIntelligence No access
2. 1. Decision Tree No access
  2. RandomForest No access
3. 1. ArtificialNeuron No access
  2. Neural Network No access
  3. Training No access
  4. Convolutional Neural Network No access
1. 1. FeatureLearning No access
  2. Random Forest Classification No access
  3. RF to NN Mapping No access
  4. Fully Convolutional Network No access
  5. Bounding Box Prediction No access
2. Localization No access
3. Clustering No access
4. 1. DataCapturing No access
  2. Filtering No access
5. 1. Training and Test Data No access
  2. Classification No access
  3. Object Detection No access
  4. ComputationTime No access
  5. Localization Accuracy No access
6. Conclusion No access
1. 1. Data Generation No access
  2. ImitationLearning No access
2. 1. Datasets No access
  2. Implementation Details No access
  3. Results No access
  4. Comparison with State of the Art No access
  5. Analysis of the Generated Data No access
3. Conclusion No access
1. 1. Encoder No access
  2. MixingModule No access
  3. Decoder No access
  4. Discriminator No access
  5. Training No access
2. 1. Experimental Setup No access
  2. Comparison with State of the Art No access
  3. Automatic Augmentation No access
  4. CLIPSynsetEntropy No access
  5. Generator Impact No access
  6. Analyses No access
  7. CLIP Features No access
  8. Hyperparameters No access
3. Conclusion No access
Conclusion No access Pages 98 - 101
Bibliography No access Pages 102 - 121