Deep Learning Papers
-CNNs/RNNs
Fully Convolutional Networks for Semantic Segmentation.[PDF]
An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognitioni.[PDF]
Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition.[PDF]
-Transformers
Language Models are Few-Shot Learners.[PDF]
-Diffusion Models
Denoising Diffusion Probabilistic Models.[PDF]
High-Resolution Image Synthesis with Latent Diffusion Models.[PDF]
-Graph Models
Semi-supervised Classification With Graph Convolutional Networks.[PDF]
-GAN Models
Conditional Generative Adversarial Nets.[PDF]
Image-to-Image Translation with Conditional Adversarial Networks.[PDF]