Convolutional networks
英文原文文件：cnn.md
计算解释
利用局部性与权重共享来适配 GPU 密集型视觉工作负载的架构家族。
支撑阅读卡
- Gradient-based learning applied to document recognition (1998,
pre_2012_cpu_statistical_foundations) - ImageNet: A large-scale hierarchical image database (2009,
pre_2012_cpu_statistical_foundations) - ImageNet Classification with Deep Convolutional Neural Networks (2012,
single_gpu_deep_learning) - Rich feature hierarchies for accurate object detection and semantic segmentation (2014,
single_gpu_deep_learning) - Very Deep Convolutional Networks for Large-Scale Image Recognition (2014,
single_gpu_deep_learning) - Going Deeper with Convolutions (2014,
single_gpu_deep_learning) - Fast R-CNN (2015,
single_gpu_deep_learning) - Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2015,
single_gpu_deep_learning) - U-Net: Convolutional Networks for Biomedical Image Segmentation (2015,
single_gpu_deep_learning) - Deep Residual Learning for Image Recognition (2015,
multi_gpu_dense_training) - Rethinking the Inception Architecture for Computer Vision (2015,
multi_gpu_dense_training) - Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (2015,
generative_media_compute) - Identity Mappings in Deep Residual Networks (2016,
multi_gpu_dense_training) - Xception: Deep Learning with Depthwise Separable Convolutions (2016,
multi_gpu_dense_training) - Densely Connected Convolutional Networks (2016,
multi_gpu_dense_training) - Image-to-Image Translation with Conditional Adversarial Networks (2016,
generative_media_compute) - SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size (2016,
efficient_edge_inference) - Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks (2017,
generative_media_compute) - MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (2017,
efficient_edge_inference) - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (2019,
multi_gpu_dense_training) - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (2020,
tpu_accelerator_transformer_era)
后续计算范式下过时或退居次要的内容
仅通过已链接的阅读卡追踪，不将本方法页视为独立证据来源。