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Table of contents
1 introduction
1.1 Context
1.2 Motivations
1.3 Contributions and outline
1.4 Related publications
2 deep neural networks for image classification: training, regularization and invariance
2.1 Introduction
2.2 Training and Regularizing Deep Neural Networks
2.3 SHADE: Encouraging Invariance in DNNs
2.4 Conclusion
3 separating discriminative and non-discriminative information for semi-supervised learning
3.1 Introduction
3.2 Reconstruction and Stability for Semi-Supervised Learning
3.3 HybridNet framework
3.4 Experiments
3.5 Conclusion
4 dual-branch structuring of the latent space for disentangling and image editing
4.1 Introduction
4.2 Related work
4.3 DualDis approach
4.4 Discussion
4.5 DualDis evaluation
4.6 Semi-Supervised Learning
4.7 Image Editing and Data Augmentation
4.8 Conclusion
5 conclusion
5.1 Summary of Contributions
5.2 Perspectives for Future Work
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