#computer-vision
10 articles
- AlexNet The deep CNN that won ImageNet 2012 and sparked the deep learning revolution
- CLIP: Contrastive Language-Image Pre-training Learning visual concepts from natural language supervision
- CS231n: CNNs for Visual Recognition Stanford's foundational course on deep learning for computer vision
- Diffusion Models Generative models that learn to denoise, enabling high-quality image and video synthesis
- Generative Adversarial Networks Two neural networks compete to generate realistic data
- Identity Mappings in Deep Residual Networks Pre-activation ResNet design that enables training of 1000+ layer networks
- Latent Diffusion Models High-resolution image generation by diffusing in learned latent spaces
- Multi-Scale Context Aggregation by Dilated Convolutions Expanding receptive fields exponentially without losing resolution or adding parameters
- ResNet Deep residual learning with skip connections that enabled training of 152+ layer networks
- Vision Transformer (ViT) Applying Transformers directly to image patches for visual recognition