Architectures * AlexNet: https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks * ZFNet: https://arxiv.org/abs/1311.2901 * VGG16: https://arxiv.org/abs/1505.06798 * ResNet: https://arxiv.org/abs/1704.06904 * GoogLeNet: https://arxiv.org/abs/1409.4842 * Inception: https://arxiv.org/abs/1512.00567 * Xception: https://arxiv.org/abs/1610.02357 * MobileNet: https://arxiv.org/abs/1704.04861 Semantic Segmentation * FCN: https://arxiv.org/abs/1411.4038 * SegNet: https://arxiv.org/abs/1511.00561 * UNet: https://arxiv.org/abs/1505.04597 * PSPNet: https://arxiv.org/abs/1612.01105 * DeepLab: https://arxiv.org/abs/1606.00915 * ICNet: https://arxiv.org/abs/1704.08545 * ENet: https://arxiv.org/abs/1606.02147 Generative adversarial networks * GAN: https://arxiv.org/abs/1406.2661 * DCGAN: https://arxiv.org/abs/1511.06434 * WGAN: https://arxiv.org/abs/1701.07875 * Pix2Pix: https://arxiv.org/abs/1611.07004 * CycleGAN: https://arxiv.org/abs/1703.10593 Object detection * RCNN: https://arxiv.org/abs/1311.2524 * Fast-RCNN: https://arxiv.org/abs/1504.08083 * Faster-RCNN: https://arxiv.org/abs/1506.01497 * SSD: https://arxiv.org/abs/1512.02325 * YOLO: https://arxiv.org/abs/1506.02640 * YOLO9000: https://arxiv.org/abs/1612.08242 Instance Segmentation * Mask-RCNN: https://arxiv.org/abs/1703.06870 * YOLACT: https://arxiv.org/abs/1904.02689 Pose estimation * PoseNet: https://arxiv.org/abs/1505.07427 * DensePose: https://arxiv.org/abs/1802.00434
链接地址：https://towardsdatascience.com/guide-to-learn-computer-vision-in-2020-36f19d92c934