- ImageNet Classification with Deep Convolutional Neural Networks [pdf] [code]
- Very Deep Convolutional Networks for Large-Scale Image Recognition [pdf] [code]
- Deep Residual Learning for Image Recognition [pdf] [code]
- Going Deeper with Convolutions [pdf] [code]
- Dynamic Routing Between Capsules [pdf] [code]
- Exploring Randomly Wired Neural Networks for Image Recognition [pdf] [code]
- AN IMAGE IS WORTH 16X16 WORDS:TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE [pdf] [code]
- Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet [pdf] [code]
- Training data-efficient image transformers & distillation through attention [pdf] [code]
- Generative Adversarial Networks [pdf] [code]
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks [pdf] [code]
- Image-to-Image Translation with Conditional Adversarial Networks [pdf] [code]
- ViTGAN: Training GANs with Vision Transformers [pdf]
- Image Super-Resolution Using Deep Convolutional Networks [pdf] [code]
- Accurate Image Super-Resolution Using Very Deep Convolutional Networks [pdf] [code]
- Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network [pdf]
- Deeply-Recursive Convolutional Network for Image Super-Resolution [pdf]
- Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network [pdf] [code]
- Enhanced Deep Residual Networks for Single Image Super-Resolution [pdf] [code]
- ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks [pdf] [code]
- RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution [pdf]
- Efficient Deep Neural Network for Photo-realistic Image Super-Resolution [pdf] [code]
- Learning Texture Transformer Network for Image Super-Resolution [pdf]
- U-Net: Convolutional Networks for Biomedical Image Segmentation [pdf] [code]
- TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation [pdf]
- SepViT: Separable Vision Transformer [pdf]
- End-to-End Object Detection with Transformers [pdf]