Изменения

* [httpshttp://arxivcs231n.orgstanford.edu/slides/abs2017/1502cs231n_2017_lecture11.02734 Weakly- pdf Stanford CS231n: Detection and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation]* [https://arxivstepik.org/abscourse/1605.06211 Fully Convolutional Networks for Semantic Segmentation]* [https:57457//arxivpromo Stepik.org/abs/1505.04597 U-Net: Convolutional Networks for Biomedical Image SegmentationDeep Learning School]* [https://arxivhabr.orgcom/absru/1611.09326 The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation]* [https:company/mipt/arxiv.orgblog/abs458190/1606.00915 DeepLabHabr: Semantic Image Segmentation with обзор Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFsLearning в Computer Vision]* [https://arxivgithub.orgcom/abshoya012/1706.05587 Rethinking Atrous Convolution for Semantic Image Segmentationdeep_learning_object_detection Список статей о детекции объектов методами глубокого обучения]* [https://paperswithcodetowardsdatascience.com/paper/encoderr-cnn-fast-r-cnn-decoderfaster-withr-atrouscnn-separable Encoderyolo-Decoder with Atrous Separable Convolution for Semantic Image Segmentation]* [https://arxiv.org/abs/1311.2524 Rich feature hierarchies for accurate object -detection and semantic segmentation]* [https://arxiv.org/abs/1504.08083 Fast -algorithms-36d53571365e R-CNN]* [https://arxiv.org/abs/1506.01497 Faster , Fast R-CNN: Towards Real-Time Object Detection with Region Proposal Networks]* [https://arxiv.org/abs/1703.06870 Mask , Faster R-CNN]* [https://arxiv.org/abs/1506.02640 You Only Look Once: Unified, Real-Time YOLO — Object DetectionAlgorithms]* [https://arxiv.org/abs/1612.08242 YOLO9000: Better, Faster, Stronger]* [https://arxiv.org/abs/1804.02767 YOLOv3: An Incremental Improvement]* [https://arxiv.org/abs/1512.02325 SSD: Single Shot MultiBox Detector]