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AlexNet- ImageNet Classification with Deep Convolutional Neural Networks, NeurIPS, 2012 -
VGGNets- Very Deep Convolutional Networks for Large-Scale Image Recognition, 2014 -
GoogLeNet- Going Deeper with Convolutions, 2014 -
Inception-V3- Rethinking the Inception Architecture for Computer Vision, 2015 -
Inception-V4 and Inception-ResNet- Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, AAAI, 2016 -
ResNet- Deep Residual Learning for Image Recognition, 2015 -
SqueezeNet- SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size, 2016 -
ResNeXt- Aggregated Residual Transformations for Deep Neural Networks, CVPR, 2016 -
Res2Net- Res2Net: A New Multi-scale Backbone Architecture, TPAMI, 2019 -
ReXNet- Rethinking Channel Dimensions for Efficient Model Design, CVPR, 2020 -
Xception- Xception: Deep Learning with Depthwise Separable Convolutions, CVPR, 2016 -
DenseNet- Densely Connected Convolutional Networks, CVPR, 2016 -
DLA- Deep Layer Aggregation, CVPR, 2017 -
DPN- Dual Path Networks, NeurIPS, 2017 -
NASNet-A- Learning Transferable Architectures for Scalable Image Recognition, CVPR, 2017 -
PNasNet- Progressive Neural Architecture Search, ECCV, 2017 -
MobileNets- MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, 2017 -
MobileNetV2- MobileNetV2: Inverted Residuals and Linear Bottlenecks, CVPR, 2018 -
MobileNetV3- Searching for MobileNetV3, ICCV, 2019 -
ShuffleNet- ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices, CVPR, 2017 -
ShuffleNetV2- ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design, ECCV, 2018 -
MnasNet- MnasNet: Platform-Aware Neural Architecture Search for Mobile, CVPR, 2018 -
GhostNet- GhostNet: More Features from Cheap Operations, CVPR, 2019 -
HRNet- Deep High-Resolution Representation Learning for Visual Recognition, TPAMI, 2019 -
CSPNet- CSPNet: A New Backbone that can Enhance Learning Capability of CNN, CVPR, 2019 -
EfficientNet- EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks, ICML, 2019 -
EfficientNetV2- EfficientNetV2: Smaller Models and Faster Training, ICML, 2021 -
RegNet- Designing Network Design Spaces, CVPR, 2020 -
GPU-EfficientNets- Neural Architecture Design for GPU-Efficient Networks, 2020 -
LambdaNetworks- LambdaNetworks: Modeling Long-Range Interactions Without Attention, ICLR, 2021 -
RepVGG- RepVGG: Making VGG-style ConvNets Great Again, CVPR, 2021 -
HardCoRe-NAS- HardCoRe-NAS: Hard Constrained diffeRentiable Neural Architecture Search, ICML, 2021 -
NFNet- High-Performance Large-Scale Image Recognition Without Normalization, ICML, 2021 -
NF-ResNets- Characterizing signal propagation to close the performance gap in unnormalized ResNets, ICLR, 2021 -
ConvMixer- Patches are all you need?, 2021 -
VGNets- Efficient CNN Architecture Design Guided by Visualization, ICME, 2022 -
ConvNeXt- A ConvNet for the 2020s, CVPR, 2022
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Non-Local- Non-local Neural Networks, CVPR, 2017 -
Squeeze-and-Excitation- Squeeze-and-Excitation Networks, CVPR, 2017 -
Gather-Excite- Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks, NeurIPS, 2018 -
CBAM- CBAM: Convolutional Block Attention Module, ECCV, 2018 -
SelectiveKernel- Selective Kernel Networks, CVPR, 2019 -
ECA- ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks, CVPR, 2019 -
GlobalContext- GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond, 2019 -
ResNeSt- ResNeSt: Split-Attention Networks, 2020 -
HaloNets- Scaling Local Self-Attention for Parameter Efficient Visual Backbones, 2021
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ViT- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, ICLR, 2020 -
DeiT- Training data-efficient image transformers & distillation through attention, ICML, 2020 -
Swin Transformer- Swin Transformer: Hierarchical Vision Transformer using Shifted Windows, ICCV, 2021 -
Twins- Twins: Revisiting the Design of Spatial Attention in Vision Transformers, NeurIPS, 2021
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MLP-Mixer- MLP-Mixer: An all-MLP Architecture for Vision, NeurIPS, 2021 -
ResMLP- ResMLP: Feedforward networks for image classification with data-efficient training, 2021 -
gMLP- Pay Attention to MLPs, 2021
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MAE- Masked Autoencoders Are Scalable Vision Learners, CVPR, 2021
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R-CNN- Rich feature hierarchies for accurate object detection and semantic segmentation, CVPR, 2013 -
Fast R-CNN- Fast R-CNN, ICCV, 2015 -
Faster R-CNN- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, 2015 -
YOLOv1- You Only Look Once: Unified, Real-Time Object Detection, 2015 -
SSD- SSD: Single Shot MultiBox Detector, ECCV, 2015 -
FPN- Feature Pyramid Networks for Object Detection, 2016
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FCN- Fully Convolutional Networks for Semantic Segmentation, CVPR, 2014 -
UNet- U-Net: Convolutional Networks for Biomedical Image Segmentation, MICCAI, 2015 -
PSPNet- Pyramid Scene Parsing Network, CVPR, 2016 -
DeepLabv3- Rethinking Atrous Convolution for Semantic Image Segmentation, 2017 -
DeepLabv3+- Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation, CVPR, 2018 -
Mask R-CNN- Mask R-CNN, 2017
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GAN- Generative Adversarial Networks, 2014 -
DCGAN- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, ICLR, 2016 -
WGAN- Wasserstein GAN, 2017
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VAE- Auto-Encoding Variational Bayes, 2013 -
CVAE- Learning Structured Output Representation using Deep Conditional Generative Models , NeurIPS, 2015 -
β-VAE- beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework, ICLR, 2017
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FGSM- Explaining and Harnessing Adversarial Examples, ICLR, 2014 -
PGD- Towards Deep Learning Models Resistant to Adversarial Attacks, ICLR, 2017