Deep Learning/Review
![[논문리뷰] High-Resolution Image Synthesis with Latent Diffusion Models (LDM, Latent Diffusion)](https://img1.daumcdn.net/thumb/R750x0/?scode=mtistory2&fname=https%3A%2F%2Fblog.kakaocdn.net%2Fdn%2FnSRa5%2FbtsMtswxVpS%2FxTyXjQoDJR5CzJdspXlNrk%2Fimg.png)
[논문리뷰] High-Resolution Image Synthesis with Latent Diffusion Models (LDM, Latent Diffusion)
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.org내용은 별거 없다. input image의 전체 domain에서 수행하던 diffusion 연산을 latent space에서 한다...
![[논문리뷰] U-Net : Convolutional Networks for Biomedical Image Segmentation](https://img1.daumcdn.net/thumb/R750x0/?scode=mtistory2&fname=https%3A%2F%2Fblog.kakaocdn.net%2Fdn%2FbNrJrs%2FbtrL9tPye4w%2Ft7E6XUyFRZOmoxdE9Kl890%2Fimg.png)
[논문리뷰] U-Net : Convolutional Networks for Biomedical Image Segmentation
대표적인 Semantic segmentation 모델인 U-Net에 대해 알아보자! 원문 링크 : https://arxiv.org/abs/1505.04597 U-Net: Convolutional Networks for Biomedical Image SegmentationThere is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available a..