文章目录
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation(ECCV2018)](#Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation(ECCV2018))
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning(2016)](#Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning(2016))
- [Wide Residual Networks(2017)](#Wide Residual Networks(2017))
- [mixup: Beyond Empirical Risk Minimization(ICLR2018)](#mixup: Beyond Empirical Risk Minimization(ICLR2018))
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](#Swin Transformer: Hierarchical Vision Transformer using Shifted Windows)
- [Pyramid Scene Parsing Network(2017)](#Pyramid Scene Parsing Network(2017))
- [Searching for MobileNetV3(2019)](#Searching for MobileNetV3(2019))
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size(2016)](#SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size(2016))
- [Identity Mappings in Deep Residual Networks(2016)](#Identity Mappings in Deep Residual Networks(2016))
- [Aggregated Residual Transformations for Deep Neural Networks](#Aggregated Residual Transformations for Deep Neural Networks)
- [MLP-Mixer: An all-MLP Architecture for Vision(2021)](#MLP-Mixer: An all-MLP Architecture for Vision(2021))
- [MOCO:Momentum Contrast for Unsupervised Visual Representation Learning](#MOCO:Momentum Contrast for Unsupervised Visual Representation Learning)
- [A ConvNet for the 2020s](#A ConvNet for the 2020s)
- [MAE:Masked Autoencoders Are Scalable Vision Learners](#MAE:Masked Autoencoders Are Scalable Vision Learners)
- [Xception: Deep Learning with Depthwise Separable Convolutions](#Xception: Deep Learning with Depthwise Separable Convolutions)
- [CLIP:Learning Transferable Visual Models From Natural Language Supervision](#CLIP:Learning Transferable Visual Models From Natural Language Supervision)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](#ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](#ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design)
- [ResNeSt: Split-Attention Networks](#ResNeSt: Split-Attention Networks)
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation(ECCV2018)
方法
DeepLabV3+结构
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning(2016)
方法
Wide Residual Networks(2017)
方法
我感觉是没啥变化
mixup: Beyond Empirical Risk Minimization(ICLR2018)
方法
主要看代码里面得lam和alpha
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
方法
Vit的滑动窗口版本
Pyramid Scene Parsing Network(2017)
Searching for MobileNetV3(2019)
方法
这是一篇关于网络架构搜索的文章
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size(2016)
方法
Identity Mappings in Deep Residual Networks(2016)
方法
讲了各种各样的跳跃连接分析
Aggregated Residual Transformations for Deep Neural Networks
方法
相当于就是参数减少
MLP-Mixer: An all-MLP Architecture for Vision(2021)
token混合和channel混合
MOCO:Momentum Contrast for Unsupervised Visual Representation Learning
采用不同存储结构,moco采用的是队列
A ConvNet for the 2020s
做到极致的卷积
MAE:Masked Autoencoders Are Scalable Vision Learners
类似于bert,预测mask部分,自监督学习
Xception: Deep Learning with Depthwise Separable Convolutions
方法
CLIP:Learning Transferable Visual Models From Natural Language Supervision
方法
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
方法
分组卷积并混合
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
方法
ResNeSt: Split-Attention Networks
本文方法