Paper2021
CLIP: Contrastive Language-Image Pre-training
Radford et al.
Learning visual concepts from natural language supervision
Vision
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Paper2021
Latent Diffusion Models
Rombach et al.
High-resolution image generation by diffusing in learned latent spaces
Concept2020
Diffusion Models
Ho, Jain & Abbeel
Generative models that learn to denoise, enabling high-quality image and video synthesis
Paper2020
Vision Transformer (ViT)
Dosovitskiy et al.
Applying Transformers directly to image patches for visual recognition
Concept2014
Generative Adversarial Networks
Goodfellow et al.
Two neural networks compete to generate realistic data
Paper2016
Identity Mappings in Deep Residual Networks
He et al.
Pre-activation ResNet design that enables training of 1000+ layer networks
Misc2015
CS231n: CNNs for Visual Recognition
Stanford
Stanford's foundational course on deep learning for computer vision
Paper2015
Multi-Scale Context Aggregation by Dilated Convolutions
Yu & Koltun
Expanding receptive fields exponentially without losing resolution or adding parameters
