Литература

Литература по алгоритмам и структурам данных:

1. Кормен Т., Лейзерсон Ч., Риверст Р., Штайн К. Алгоритмы. Построение и анализ. - М.: Издательский дом "Вильямс". - 2005. - 1290с.

2. Окулов С.М. Программирование в алгоритмах. - М.: БИНОМ. Лаборатория знаний. - 2004. - 341с.

3. Алексеев В.Е., Таланов В.А. Графы. Модели вычислений. Алгоритмы. - Н.Новгород: Изд-во Нижегородского госуниверситета. - 2005. - 307с.

Литература по программированию на языке С# и платформе .NET:

1. Троелсон Э. Язык программирования C# 2005 и платформа .NET 2.0. - М.: Издательский дом "Вильямс". - 2007. - 1167с.

2. Троелсон Э. C# и платформа .NET. - СПб.: Питер. - 2004. - 796с.

3. Маклин С., Нафтел Дж., Уильямс К. Microsoft .NET Remoting. - М.: Издательско-торговый дом "Русская редакция". - 2003. - 355с.

Литература по программированию на языке С++:

1. Павловская Т.А. С/С++. Программирование на языке высокого уровня. - СПб.: Питер. - 2003. - 460с.

Литература по шаблонам проектирования:

1. Гамма Э., Хелм Р., Джонсон Р., Влиссидес Дж. Приемы объектно-ориентированного проектирования. Паттерны проектирования. - СПб.: Питер. - 2001. - 366с.

Литература по программной инженерии:

1. Форд Н. "Продуктивный программист".

2. Хант Э., Томас Д. "Программист-прагматик".

3. Гудлиф П. "Ремесло программиста".

4. Макконелл С. "Совершенный код".

Литература по глубокому обучению:

1. Хайкин С. Нейронные сети. Полный курс. – М.: Издательский дом «Вильямс». – 2006. – 1104 с.

2. Осовский С. Нейронные сети для обработки информации. – М.: Финансы и статистика. – 2002. – 344 с.

3. Goodfellow I., Bengio Y., Courville A. DeepLearning. – MIT Press. – 2016. – [http://www.deeplearningbook.org].

4. Николенко С., Кадурин А., Архангельская Е. Глубокое обучение. Погружение в мир нейронных сетей. – Изд-во «Питер». – 2018. – 476 с.

Статьи с решением классических задач компьютерного зрения средствами глубокого обучения:

Классификация изображений

1. Krizhevsky A., Sutskever I., Hinton G.E. ImageNet Classification with Deep Convolutional Neural Networks // Advances in neural information processing systems. – 2012. – [http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf].

2. Sermanet P., Eigen D., Zhang X., Mathieu M., Fergus R., LeCun Y. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks. –2013. – [https://arxiv.org/pdf/1312.6229.pdf].

3. Simonyan K., Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition. – 2014. –[https://arxiv.org/pdf/1409.1556.pdf].

4. Szegedy C., Liu W., Jia Y., Sermanet P., Reed S., Anguelov D., Erhan D., Vanhoucke V., Rabinovich A. Going Deeper with Convolutions. – 2014. – [https://arxiv.org/pdf/1409.4842.pdf].

5. He K., Zhang X., Ren S., Sun J. Deep Residual Learning for Image Recognition. – 2015. – [https://arxiv.org/pdf/1512.03385.pdf].

6. Ioffe S., Szegedy C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. – 2015. –[https://arxiv.org/pdf/1502.03167.pdf].

7. Szegedy C., Vanhoucke V., Ioffe S., Shlens J., Wojna Z. Rethinking the Inception Architecture for Computer Vision. – 2015. –[https://arxiv.org/pdf/1512.00567.pdf], [https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Szegedy_Rethinking_the_Inception_CVPR_2016_paper.pdf].

8. Huang G., Liu Z., Maaten L., Weinberger K.Q. Densely Connected Convolutional Networks. – 2016. – [https://arxiv.org/pdf/1608.06993.pdf].

9. Chollet F. Xception: Deep Learning with Depthwise Separable Convolutions. – 2016. – [https://arxiv.org/pdf/1610.02357.pdf].

10. Howard A.G., et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. – 2017. –[https://arxiv.org/pdf/1704.04861.pdf].

11. Xie S., Girshick R., Dollar P., Tu Z., He K. Aggregated Residual Transformations for Deep Neural Networks. – 2017. –[https://arxiv.org/pdf/1611.05431v2.pdf], [https://ieeexplore.ieee.org/document/8100117].

12. Sandler M., Howard A., Zhu M., Zhmoginov A., Chen L.-C. MobileNetV2: Inverted Residuals and Linear Bottlenecks. – 2018. –[https://arxiv.org/pdf/1801.04381.pdf], [https://ieeexplore.ieee.org/document/8578572].

13. Tan M., Le Q.V. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. – 2019. – [https://arxiv.org/pdf/1905.11946.pdf].

14. Bianco S., Cadene R., Celona L., Napoletano P. Benchmark Analysis of Representative Deep Neural Network Architectures. – 2018. –[https://arxiv.org/pdf/1810.00736.pdf].

Детектирование объектов

1. Girshick R., Donahue J., Darrell T., Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation. – 2014. –[https://arxiv.org/pdf/1311.2524.pdf], [https://ieeexplore.ieee.org/abstract/document/6909475].

2. Girshick R. Fast R-CNN. – 2015. – [https://arxiv.org/pdf/1504.08083.pdf], [https://ieeexplore.ieee.org/document/7410526].

3. Ren S., He K., Girshick R., Sun J. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. – 2016. –[https://arxiv.org/pdf/1506.01497.pdf], [https://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf].

4. Dai J., Li Y., He K., Sun J. R-FCN: Object Detection via Region-based Fully Convolutional Networks. – 2016. –[https://arxiv.org/pdf/1605.06409.pdf], [https://papers.nips.cc/paper/6465-r-fcn-object-detection-via-region-based-fully-convolutional-networks.pdf].

5. Liu W., Anguelov D., Erhan D., SzegedyC., Reed S., Fu C.-Y., Berg A.C. SSD: Single Shot MultiBoxDetector. – 2016. –[https://arxiv.org/pdf/1512.02325.pdf], [https://link.springer.com/chapter/10.1007/978-3-319-46448-0_2].

6. Redmon J., Divvala S., Girshick R., Farhadi A. You only look once: Unified, real-time object detection. – 2015. –[https://arxiv.org/pdf/1506.02640.pdf], [https://ieeexplore.ieee.org/document/7780460].

7. Redmon J., Farhadi A. YOLO9000: Better, Faster, Stronger. – 2016. – [https://arxiv.org/pdf/1612.08242.pdf], [https://pjreddie.com/darknet/yolo].

8. Redmon J., Farhadi A. YOLOv3: An Incremental Improvement. – 2018. – [https://pjreddie.com/media/files/papers/YOLOv3.pdf].

9. Lin T., Goyal P., Girshick R., He K., Dollar P. Focal Loss for Dense Object Detection. – 2018. – [https://arxiv.org/pdf/1708.02002.pdf].

10. Zhou X., Wang D., Krahenbuhl P. Objects as Points. – 2019. – [https://arxiv.org/pdf/1904.07850.pdf].

Семантическая сегментация изображений

1. Long J., Shelhamer E., Darrel T. Fully Convolutional Networks for Semantic Segmentation. – 2015. – [https://arxiv.org/pdf/1411.4038.pdf], [https://ieeexplore.ieee.org/document/7298965].

2. Badrinarayanan V., Kendall A., Cipolla R. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. – 2015. – [https://arxiv.org/pdf/1511.00561.pdf], [https://ieeexplore.ieee.org/document/7803544].

3. Ronneberger O., Fischer P., Brox T. U-net: Convolutional networks for biomedical image segmentation. – 2015. –[https://arxiv.org/pdf/1505.04597.pdf], [https://link.springer.com/chapter/10.1007/978-3-319-24574-4_28].

4. Zhao H., Shi J., Qi X., Wang X., Jia J. Pyramid scene parsing network. – 2016. – [https://arxiv.org/pdf/1612.01105.pdf], [https://ieeexplore.ieee.org/document/8100143].

5. Zhao H., Qi X., Shen X., Shi J., Jia J. ICNet for Real-Time Semantic Segmentation on High-Resolution Images. –2017. –[https://arxiv.org/pdf/1704.08545.pdf], [https://link.springer.com/chapter/10.1007/978-3-030-01219-9_25].

6. Chen L.-C., Papandreou G., Kokkinos I., Murphy K., Yuille A.L. Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs. – 2014. – [https://arxiv.org/pdf/1412.7062.pdf].

7. Chen L.-C., Papandreou G., Kokkinos I., Murphy K., Yuille A.L. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, AtrousConvolution, and Fully Connected CRFs. – 2017.– [https://arxiv.org/pdf/1606.00915.pdf], [https://ieeexplore.ieee.org/document/7913730].

8. Chen L.-C., Papandreou G., Schroff F., Adam H. Rethinking AtrousConvolution for Semantic Image Segmentation. – 2017. –[https://arxiv.org/pdf/1706.05587.pdf].

9. Chen L.-C., Zhu Y., Papandreou G., Schoff F., Adam H. Encoder-Decoder with AtrousSeparable Convolution for Semantic Image Segmentation. – 2018. – [https://arxiv.org/pdf/1802.02611.pdf].

10. Zheng S., Jayasumana S., Romera-Paredes B., Vineet V., Su Z., Du D., Huang C., Torr P. Conditional Random Fields as Recurrent Neural Networks. – 2016. – [https://arxiv.org/pdf/1502.03240v3.pdf].

11. Fisher Yu, Koltun V. Multi-Scale Context Aggregation by Dilated Convolutions. – 2016. – [https://arxiv.org/pdf/1511.07122v3.pdf].

12. Paszke A., Chaurasia A., Kim S., Culurciello E. ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation. – 2016. –[https://arxiv.org/pdf/1606.02147v1.pdf].

13. Pohlen T., Hermans A., Mathias M., Leibe B. Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes. – 2016. –[https://arxiv.org/pdf/1611.08323v2.pdf].

14. Berman M., Triki A., Blaschko M. The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks. – 2018. – [https://arxiv.org/pdf/1705.08790v2.pdf].

15. Wang Y., Zhou Q., Liu J., Xiong J., Gao G., Wu X., Latecki L. LEDNet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation. – 2019. – [https://arxiv.org/pdf/1905.02423v3.pdf].

16. Wang Y., Zhou Q., Wu X. ESNet: An Efficient Symmetric Network for Real-time Semantic Segmentation. – 2019. –[https://arxiv.org/pdf/1906.09826v1.pdf].