@@ -25,7 +25,7 @@ Keras [1]是一个受欢迎的深度学习库，在撰写本文时有 370,000
我们选择`tf.keras`作为本书的首选工具，因为它是致力于加速深度学习模型实施的库。 这使得 Keras 非常适合我们想要实用且动手的时候，例如，当我们探索本书中的高级深度学习概念时。 由于 Keras 旨在加速深度学习模型的开发，培训和验证，因此在有人可以最大限度地利用图书馆之前，必须学习该领域的关键概念。
本书没有涵盖完整的 Keras API。 我们将仅介绍解释本书中选定的高级深度学习主题所需的材料。 有关更多信息，请查阅 Keras 官方文档，该文档可在 [https://keras.io](https://keras.io) 或 [https://www.tensorflow.org/guide/keras/overview [](https://www.tensorflow.org/guide/keras/overview)。
本书没有涵盖完整的 Keras API。 我们将仅介绍解释本书中选定的高级深度学习主题所需的材料。 有关更多信息，请查阅 Keras 官方文档，该文档在[这里](https://keras.io)或[这里](https://www.tensorflow.org/guide/keras/overview)。
1.`Kaiming He et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. Proceedings of the IEEE international conference on computer vision, 2015 (https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdfspm=5176.100239.blogcont55892.28.pm8zm1&file=He_Delving_Deep_into_ICCV_2015_paper.pdf).`
1.`Kaiming He et al. Deep Residual Learning for Image Recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, 2016a (http://openaccess.thecvf.com/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf).`
1.`Karen Simonyan and Andrew Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition. ICLR, 2015 (https://arxiv.org/pdf/1409.1556/).`
1. K`aiming He et al. Identity Mappings in Deep Residual Networks. European Conference on Computer Vision. Springer International Publishing, 2016b (https://arxiv.org/pdf/1603.05027.pdf).`
1.`Gao Huang et al. Densely Connected Convolutional Networks. Proceedings of the IEEE conference on computer vision and pattern recognition, 2017 (http://openaccess.thecvf.com/content_cvpr_2017/papers/Huang_Densely_Connected_Convolutional_CVPR_2017_paper.pdf).`
1.`Saining Xie et al. Aggregated Residual Transformations for Deep Neural Networks. Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on. IEEE, 2017 (http://openaccess.thecvf.com/content_cvpr_2017/papers/Xie_Aggregated_Residual_Transformations_CVPR_2017_paper.pdf).`
1.`Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv preprint arXiv:1511.06434, 2015 (https://arxiv.org/pdf/1511.06434.pdf).`
1.`Mehdi Mirza and Simon Osindero. Conditional Generative Adversarial Nets. arXiv preprint arXiv:1411.1784, 2014 (https://arxiv.org/pdf/1411.1784.pdf).`
1.`Tero Karras et al. Progressive Growing of GANs for Improved Quality, Stability, and Variation. ICLR, 2018 (https://arxiv.org/pdf/1710.10196.pdf).`
1.`Tero Karras, , Samuli Laine, and Timo Aila. A Style-Based Generator Architecture for Generative Adversarial Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.`
1.`Tero Karras et al. Analyzing and Improving the Image Quality of StyleGAN. 2019 (https://arxiv.org/abs/1912.04958).`
1.`Ian Goodfellow et al.: Generative Adversarial Nets. Advances in neural information processing systems, 2014 (http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf).`
1.`Xudong Mao et al.: Least Squares Generative Adversarial Networks. 2017 IEEE International Conference on Computer Vision (ICCV). IEEE 2017 (http://openaccess.thecvf.com/content_ICCV_2017/papers/Mao_Least_Squares_Generative_ICCV_2017_paper.pdf).`
1.`Augustus Odena, Christopher Olah, and Jonathon Shlens. Conditional Image Synthesis with Auxiliary Classifier GANs. ICML, 2017 (http://proceedings.mlr.press/v70/odena17a/odena17a.pdf).`
1.`Xi Chen et al.: InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. Advances in Neural Information Processing Systems, 2016 (http://papers.nips.cc/paper/6399-infogan-interpretable-representation-learning-by-information-maximizing-generative-adversarial-nets.pdf).`
1.`Xun Huang et al. Stacked Generative Adversarial Networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Vol. 2, 2017 (http://openaccess.thecvf.com/content_cvpr_2017/papers/Huang_Stacked_Generative_Adversarial_CVPR_2017_paper.pdf).`
1.`Yuval Netzer et al.: Reading Digits in Natural Images with Unsupervised Feature Learning. NIPS workshop on deep learning and unsupervised feature learning. Vol. 2011. No. 2. 2011 (https://www-cs.stanford.edu/~twangcat/papers/nips2011_housenumbers.pdf).`
1.`Zhu-Jun-Yan et al.: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017 (http://openaccess.thecvf.com/content_ICCV_2017/papers/Zhu_Unpaired_Image-To-Image_Translation_ICCV_2017_paper.pdf).`
1.`Phillip Isola et al.: Image-to-Image Translation with Conditional Adversarial Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017 (http://openaccess.thecvf.com/content_cvpr_2017/papers/Isola_Image-To-Image_Translation_With_CVPR_2017_paper.pdf).`
1.`Mehdi Mirza and Simon Osindero. Conditional Generative Adversarial Nets. arXiv preprint arXiv:1411.1784, 2014 (https://arxiv.org/pdf/1411.1784.pdf).`
1.`Xudong Mao et al.: Least Squares Generative Adversarial Networks. 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017 (http://openaccess.thecvf.com/content_ICCV_2017/papers/Mao_Least_Squares_Generative_ICCV_2017_paper.pdf).`
1.`Chuan Li and Michael Wand. Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks. European Conference on Computer Vision. Springer, Cham, 2016 (https://arxiv.org/pdf/1604.04382.pdf).`
1.`Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation. International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015 (https://arxiv.org/pdf/1505.04597.pdf).`
1.`Diederik P. Kingma and Max Welling. Auto-encoding Variational Bayes. arXiv preprint arXiv:1312.6114, 2013 (https://arxiv.org/pdf/1312.6114.pdf).`
1.`Kihyuk Sohn, Honglak Lee, and Xinchen Yan. Learning Structured Output Representation Using Deep Conditional Generative Models. Advances in Neural Information Processing Systems, 2015 (http://papers.nips.cc/paper/5775-learning-structured-output-representation-using-deep-conditional-generative-models.pdf).`
1.`Yoshua Bengio, Aaron Courville, and Pascal Vincent. Representation Learning.`
1.`A Review and New Perspectives. IEEE transactions on Pattern Analysis and Machine Intelligence 35.8, 2013: 1798-1828 (https://arxiv.org/pdf/1206.5538.pdf).`
1.`Xi Chen et al.: Infogan: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. Advances in Neural Information Processing Systems, 2016 (http://papers.nips.cc/paper/6399-infogan-interpretable-representation-learning-by-information-maximizing-generative-adversarial-nets.pdf).`
1.`I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner. -VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. ICLR, 2017 (https://openreview.net/pdf?id=Sy2fzU9gl).`
3. Hado Van Hasselt，Arthur Guez 和 David Silver：*带有双重 Q 学习的深度强化学习*。 AAAI。 卷 2016 年 6 月 16 日（ [http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/download/12389/11847](http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/download/12389/11847) ）。
4. Kai Arulkumaran 等：*深度强化学习*的简要概述。 arXiv 预印本 arXiv：1708.05866，2017（ [https://arxiv.org/pdf/1708.05866.pdf](https://arxiv.org/pdf/1708.05866.pdf) ）。
5. David Silver：*关于强化学习的讲义*（ [http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html) ）。
6. Tom Schaul 等：*优先体验重播*。 arXiv 预印本 arXiv：1511.05952，2015（ [https://arxiv.org/pdf/1511.05952.pdf](https://arxiv.org/pdf/1511.05952.pdf) ）。
1. Wang Ziyu 等人：*深度强化学习的决斗网络架构*。 arXiv 预印本 arXiv：1511.06581，2015（ [https://arxiv.org/pdf/1511.06581.pdf](https://arxiv.org/pdf/1511.06581.pdf) ）。
\ No newline at end of file
1.`Sutton and Barto: Reinforcement Learning: An Introduction, 2017 (http://incompleteideas.net/book/bookdraft2017nov5.pdf).`
1.`Volodymyr Mnih et al.: Human-level Control through Deep Reinforcement Learning. Nature 518.7540, 2015: 529 (http://www.davidqiu.com:8888/research/nature14236.pdf).`
1.`Hado Van Hasselt, Arthur Guez, and David Silver: Deep Reinforcement Learning with Double Q-Learning. AAAI. Vol. 16, 2016 (http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/download/12389/11847).`
1.`Kai Arulkumaran et al.: A Brief Survey of Deep Reinforcement Learning. arXiv preprint arXiv:1708.05866, 2017 (https://arxiv.org/pdf/1708.05866.pdf).`
1.`David Silver: Lecture Notes on Reinforcement Learning (http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html).`
1.`Ziyu Wang et al.: Dueling Network Architectures for Deep Reinforcement Learning. arXiv preprint arXiv:1511.06581, 2015 (https://arxiv.org/pdf/1511.06581.pdf).`
1.`Krizhevsky Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.`
1.`Liu Wei, et al. "SSD: Single Shot MultiBox Detector." European conference on computer vision. Springer, Cham, 2016.`
1.`Girshick Ross. "Fast R-CNN." Proceedings of the IEEE international conference on computer vision. 2015.`
1.`Lin Tsung-Yi, et al. "Focal loss for Dense Object Detection. "Proceedings of the IEEE international conference on computer vision. 2017.`
1.`Dutta, et al. VGG Image Annotator http://www.robots.ox.ac.uk/~vgg/software/via/`
1.`Bodla Navaneeth, et al. "Soft-NMS--Improving Object Detection With One Line of Code." Proceedings of the IEEE international conference on computer vision. 2017.`
1.`Everingham Mark, et al. "The Pascal Visual Object Classes (VOC) challenge." International journal of computer vision 88.2 (2010): 303-338.`
1.`Kirillov, Alexander, et al.: Panoptic Segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition. 2019.`
1. L`ong, Jonathan, Evan Shelhamer, and Trevor Darrell: Fully Convolutional Networks for Semantic Segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.`
1.`Zhao, Hengshuang, et al.: Pyramid Scene Parsing Network. Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.`
1.`Dutta, et al.: VGG Image Annotator http://www.robots.ox.ac.uk/~vgg/software/via/`
1.`He Kaiming, et al.: Mask R-CNN. Proceedings of the IEEE international conference on computer vision. 2017.`
1.`Ji, Xu, João F. Henriques, and Andrea Vedaldi. Invariant Information Clustering for Unsupervised Image Classification and Segmentation. International Conference on Computer Vision, 2019.`
1.`Simonyan, Karen, and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).`
1.`Belghazi, Mohamed Ishmael, et al. Mutual Information Neural Estimation. International Conference on Machine Learning. 2018.`
