深度学习图像处理相关文献

偏执的太偏执、 2022-11-12 04:22 169阅读 0赞

## 本文致力于收集深度学习图像处理方面的深度学习神经网络的文献。 ##

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## 图像分类(Classification) ##

*  AlexNet [http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf][http_papers.nips.cc_paper_4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf]
 *  VGG [https://arxiv.org/abs/1409.1556][https_arxiv.org_abs_1409.1556]
 *  GoogLeNet, Inceptionv1(Going deeper with convolutions) [https://arxiv.org/abs/1409.4842][https_arxiv.org_abs_1409.4842]
 *  Batch Normalization [https://arxiv.org/abs/1502.03167][https_arxiv.org_abs_1502.03167]
 *  Inceptionv3(Rethinking the Inception Architecture for Computer Vision) [https://arxiv.org/abs/1512.00567][https_arxiv.org_abs_1512.00567]
 *  Inceptionv4, Inception-ResNet [https://arxiv.org/abs/1602.07261][https_arxiv.org_abs_1602.07261]
 *  Xception(Deep Learning with Depthwise Separable Convolutions) [https://arxiv.org/abs/1610.02357][https_arxiv.org_abs_1610.02357]
 *  ResNet [https://arxiv.org/abs/1512.03385][https_arxiv.org_abs_1512.03385]
 *  ResNeXt [https://arxiv.org/abs/1611.05431][https_arxiv.org_abs_1611.05431]
 *  DenseNet [https://arxiv.org/abs/1608.06993][https_arxiv.org_abs_1608.06993]
 *  NASNet-A(Learning Transferable Architectures for Scalable Image Recognition) [https://arxiv.org/abs/1707.07012][https_arxiv.org_abs_1707.07012]
 *  SENet(Squeeze-and-Excitation Networks) [https://arxiv.org/abs/1709.01507][https_arxiv.org_abs_1709.01507]
 *  MobileNet(v1) [https://arxiv.org/abs/1704.04861][https_arxiv.org_abs_1704.04861]
 *  MobileNet(v2) [https://arxiv.org/abs/1801.04381][https_arxiv.org_abs_1801.04381]
 *  MobileNet(v3) [https://arxiv.org/abs/1905.02244][https_arxiv.org_abs_1905.02244]
 *  ShuffleNet(v1) [https://arxiv.org/abs/1707.01083][https_arxiv.org_abs_1707.01083]
 *  ShuffleNet(v2) [https://arxiv.org/abs/1807.11164][https_arxiv.org_abs_1807.11164]
 *  EfficientNet [https://arxiv.org/abs/1905.11946][https_arxiv.org_abs_1905.11946]
 *  CSPNet [https://arxiv.org/abs/1911.11929][https_arxiv.org_abs_1911.11929]
 *  RegNet [https://arxiv.org/abs/2003.13678][https_arxiv.org_abs_2003.13678]
 *  NFNets(High-Performance Large-Scale Image Recognition Without Normalization) [https://arxiv.org/abs/2102.06171][https_arxiv.org_abs_2102.06171]

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## 目标检测(Object Detection) ##

*  R-CNN [https://arxiv.org/abs/1311.2524][https_arxiv.org_abs_1311.2524]
 *  Fast R-CNN [https://arxiv.org/abs/1504.08083][https_arxiv.org_abs_1504.08083]
 *  Faster R-CNN [https://arxiv.org/abs/1506.01497][https_arxiv.org_abs_1506.01497]
 *  Mask R-CNN [https://arxiv.org/abs/1703.06870][https_arxiv.org_abs_1703.06870]
 *  SSD [https://arxiv.org/abs/1512.02325][https_arxiv.org_abs_1512.02325]
 *  FPN(Feature Pyramid Networks for Object Detection) [https://arxiv.org/abs/1612.03144][https_arxiv.org_abs_1612.03144]
 *  RetinaNet(Focal Loss for Dense Object Detection) [https://arxiv.org/abs/1708.02002][https_arxiv.org_abs_1708.02002]
 *  YOLOv1 [https://arxiv.org/abs/1506.02640][https_arxiv.org_abs_1506.02640]
 *  YOLOv2 [https://arxiv.org/abs/1612.08242][https_arxiv.org_abs_1506.02640]
 *  YOLOv3 [https://arxiv.org/abs/1804.02767][https_arxiv.org_abs_1804.02767]
 *  YOLOv4 [https://arxiv.org/abs/2004.10934][https_arxiv.org_abs_2004.10934]

## Others ##

*  Microsoft COCO: Common Objects in Context [https://arxiv.org/abs/1405.0312][https_arxiv.org_abs_1405.0312]
 *  The PASCALVisual Object Classes Challenge: A Retrospective [http://host.robots.ox.ac.uk/pascal/VOC/pubs/everingham15.pdf][http_host.robots.ox.ac.uk_pascal_VOC_pubs_everingham15.pdf]

[http_papers.nips.cc_paper_4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf]: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
[https_arxiv.org_abs_1409.1556]: https://arxiv.org/abs/1409.1556
[https_arxiv.org_abs_1409.4842]: https://arxiv.org/abs/1409.4842
[https_arxiv.org_abs_1502.03167]: https://arxiv.org/abs/1502.03167
[https_arxiv.org_abs_1512.00567]: https://arxiv.org/abs/1512.00567
[https_arxiv.org_abs_1602.07261]: https://arxiv.org/abs/1602.07261
[https_arxiv.org_abs_1610.02357]: https://arxiv.org/abs/1610.02357
[https_arxiv.org_abs_1512.03385]: https://arxiv.org/abs/1512.03385
[https_arxiv.org_abs_1611.05431]: https://arxiv.org/abs/1611.05431
[https_arxiv.org_abs_1608.06993]: https://arxiv.org/abs/1608.06993
[https_arxiv.org_abs_1707.07012]: https://arxiv.org/abs/1707.07012
[https_arxiv.org_abs_1709.01507]: https://arxiv.org/abs/1709.01507
[https_arxiv.org_abs_1704.04861]: https://arxiv.org/abs/1704.04861
[https_arxiv.org_abs_1801.04381]: https://arxiv.org/abs/1801.04381
[https_arxiv.org_abs_1905.02244]: https://arxiv.org/abs/1905.02244
[https_arxiv.org_abs_1707.01083]: https://arxiv.org/abs/1707.01083
[https_arxiv.org_abs_1807.11164]: https://arxiv.org/abs/1807.11164
[https_arxiv.org_abs_1905.11946]: https://arxiv.org/abs/1905.11946
[https_arxiv.org_abs_1911.11929]: https://arxiv.org/abs/1911.11929
[https_arxiv.org_abs_2003.13678]: https://arxiv.org/abs/2003.13678
[https_arxiv.org_abs_2102.06171]: https://arxiv.org/abs/2102.06171
[https_arxiv.org_abs_1311.2524]: https://arxiv.org/abs/1311.2524
[https_arxiv.org_abs_1504.08083]: https://arxiv.org/abs/1504.08083
[https_arxiv.org_abs_1506.01497]: https://arxiv.org/abs/1506.01497
[https_arxiv.org_abs_1703.06870]: https://arxiv.org/abs/1703.06870
[https_arxiv.org_abs_1512.02325]: https://arxiv.org/abs/1512.02325
[https_arxiv.org_abs_1612.03144]: https://arxiv.org/abs/1612.03144
[https_arxiv.org_abs_1708.02002]: https://arxiv.org/abs/1708.02002
[https_arxiv.org_abs_1506.02640]: https://arxiv.org/abs/1506.02640
[https_arxiv.org_abs_1804.02767]: https://arxiv.org/abs/1804.02767
[https_arxiv.org_abs_2004.10934]: https://arxiv.org/abs/2004.10934
[https_arxiv.org_abs_1405.0312]: https://arxiv.org/abs/1405.0312
[http_host.robots.ox.ac.uk_pascal_VOC_pubs_everingham15.pdf]: http://host.robots.ox.ac.uk/pascal/VOC/pubs/everingham15.pdf