Paul's Grit

https://arxiv.org/abs/2102.12092 Zero-Shot Text-to-Image GenerationText-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentatiarxiv.org https://github.com/lucidrains/DALLE-pytorch GitHub - lucidrains/D..

Paperhttps://arxiv.org/abs/2103.00020 Learning Transferable Visual Models From Natural Language SupervisionState-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual coarxiv.org  OpenAI CLIP Documenthttp..

해당 게시물은 아래의 Hugging Face 자료를 일부 번역한 것입니다.https://huggingface.co/blog/vision_language_pretraining A Dive into Vision-Language ModelsA Dive into Vision-Language Models Human learning is inherently multi-modal as jointly leveraging multiple senses helps us understand and analyze new information better. Unsurprisingly, recent advances in multi-modal learning take inspiration from the effehuggingface..

https://arxiv.org/abs/2112.10752 High-Resolution Image Synthesis with Latent Diffusion ModelsBy decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism tarxiv.org 1. Introduction기존의 Diffusion-based mode..

Jonathan Ho, Ajay Jain, Pieter Abbeel UC Berkeley NeurIPS 2020 [Paper] 1. Introduction Diffusion Probabilistic Models (이하 Diffusion Models)에는 forward process와 reverse process가 있다.그림에서처럼 forward process는 image to noise, reverse process는 noise to image의 과정이다.이때, 딥러닝에 의해 paramieterize되는 부분은 reverse process이다. 2. Background2.1 Forward (Diffusion) Process본 논문에서 Diffusion process $q(x_{1:T}|x_{0})$는 M..