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59 3.4 .1 Datasets and data processing ,
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, 7 .2 Image generation for Data Augmentation
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Information Dropout: learning optimal representations through noisy computation, vol.32, p.23, 2016. ,
What regularized auto-encoders learn from the data-generating distribution, Journal of Machine Learning Research, p.25, 2014. ,
Improving Inception and Image Classification in TensorFlow, p.17, 2016. ,
Deep Variational Information Bottleneck, International Conference on Learning Representations (ICLR) (cit, vol.32, p.23, 2017. ,
The effects of adding noise during backpropagation training on a generalization performance, Neural computation (cit, p.20, 1996. ,
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MUTAN: Multimodal Tucker Fusion for Visual Question Answering, 2017 IEEE International Conference on Computer Vision (ICCV), 2017. ,
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Study on training methods and generalization performance of deep learning for image classification, vol.13, p.11, 2018. ,
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Shade: Information-Based Regularization for Deep Learning, 2018 25th IEEE International Conference on Image Processing (ICIP), p.31, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01994740
Shade: Information-Based Regularization for Deep Learning, 2018 25th IEEE International Conference on Image Processing (ICIP), vol.11, p.9, 2018. ,
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Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.107, 2017. ,
Large scale gan training for high fidelity natural image synthesis, International Conference on Learning Representations (ICLR) (cit, p.82, 2019. ,
Invariant Scattering Convolution Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.8, pp.1872-1886, 2013. ,
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All About Structure: Adapting Structural Information Across Domains for Boosting Semantic Segmentation, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), vol.107, p.77, 2019. ,
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.40, issue.4, pp.834-848, 2018. ,
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InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets, Advances in Neural Information Processing Systems (NIPS) (cit, p.129, 2016. ,
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StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, p.81, 2018. ,
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Deep sparse rectifier neural networks, International Conference on Artificial Intelligence and Statistics (AISTATS) (cit, p.25, 2011. ,
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Topdown regularization of deep belief networks, Advances in Neural Information Processing Systems (NIPS) (cit, p.25, 2013. ,
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A Two-Step Disentanglement Method, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, vol.86, p.91, 2018. ,
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Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol.4, p.37, 2016. ,
AttGAN: Facial Attribute Editing by Only Changing What You Want, IEEE Transactions on Image Processing, vol.28, issue.11, pp.5464-5478, 2019. ,
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Disentangling Factors of Variation by Mixing Them, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (cit, p.84, 2018. ,
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, Journal of Machine Learning Research, 2016. ,
Label Propagation for Deep Semi-Supervised Learning, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), vol.97, p.24, 2019. ,
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Dynamic Steerable Blocks in Deep Residual Networks, Procedings of the British Machine Vision Conference 2017, vol.108, p.51, 2017. ,
Unsupervised Adversarial Invariance, Advances in Neural Information Processing Systems (NeurIPS) (cit. on pp, vol.27, p.91, 2018. ,
Shakeout: A New Approach to Regularized Deep Neural Network Training, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.40, issue.5, pp.1245-1258, 2018. ,
Analyzing and Improving the Image Quality of StyleGAN, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), p.81, 2020. ,
A Style-Based Generator Architecture for Generative Adversarial Networks, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), vol.81, 2019. ,
Scaling laws and local minima in Hebbian ICA, Advances in Neural Information Processing Systems 14, 2002. ,
Preprint repository arXiv achieves milestone million uploads, Physics Today, p.83, 2014. ,
Adam: A method for stochastic optimization, International Conference on Learning Representations (ICLR) (cit, p.15, 2015. ,
Glow: Generative flow with invertible 1x1 convolutions, Advances in Neural Information Processing Systems (NeurIPS) (cit, p.109, 2018. ,
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An Introduction to Variational Autoencoders, International Conference on Learning Representations (ICLR) (cit. on pp. 25, vol.80, 2019. ,
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UberNet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. ,
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Multi-task Adversarial Network for Disentangled Feature Learning, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, vol.27, p.91, 2018. ,
Exploring Disentangled Feature Representation Beyond Face Identification, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, vol.84, 2018. ,
Deep Learning Face Attributes in the Wild, 2015 IEEE International Conference on Computer Vision (ICCV), p.92, 2015. ,
The variational fair autoencoder, International Conference on Learning Representations (ICLR) (cit, p.77, 2016. ,
The expressive power of neural networks: A view from the width, Advances in Neural Information Processing Systems (NIPS) (cit. on p, vol.18, 2017. ,
Adversarial training of partially invertible variational autoencoders, 2019. ,
Understanding the effective receptive field in deep convolutional neural networks, Advances in Neural Information Processing Systems (NIPS) (cit, p.16, 2016. ,
Human pose regression by combining indirect part detection and contextual information, Computers & Graphics, vol.85, pp.15-22, 2019. ,
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Manifold Learning in Quotient Spaces, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. ,
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.41, issue.8, pp.1979-1993, 2019. ,
Invariant Representations: Mathematics of Invariance, Visual Cortex and Deep Networks, p.20, 2016. ,
Reading digits in natural images with unsupervised feature learning, Advances in Neural Information Processing Systems Workshop (NIPS-W) (cit, p.60, 2011. ,
Greedy Layer-Wise Training of Deep Networks, Advances in Neural Information Processing Systems 19, p.20, 2007. ,
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Achieving remanufacturing inspection using deep learning, Journal of Remanufacturing, p.15, 2020. ,
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Jigsaw Puzzle Solving Using Local Feature Co-Occurrences in Deep Neural Networks, 2018 25th IEEE International Conference on Image Processing (ICIP), p.53, 2018. ,
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Reconstruction-Based Disentanglement for Pose-Invariant Face Recognition, 2017 IEEE International Conference on Computer Vision (ICCV), vol.84, p.27, 2017. ,
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