반응형 Paper Review/Generative Model3 [논문리뷰] DDT : Decoupled Diffusion Trasnformer DDT: Decoupled Diffusion TransformerDiffusion transformers have demonstrated remarkable generation quality, albeit requiring longer training iterations and numerous inference steps. In each denoising step, diffusion transformers encode the noisy inputs to extract the lower-frequency semanticarxiv.org오늘 리뷰할 논문은 DDT라고 해서, 기존의 DiT / SiT 등 Transformer 기반의 Diffusion Process에서의 한계점을 극복하기 위한 논문이라고 할 수 .. 2025. 4. 17. [논문리뷰] Fractal Generative Models Fractal Generative ModelsModularization is a cornerstone of computer science, abstracting complex functions into atomic building blocks. In this paper, we introduce a new level of modularization by abstracting generative models into atomic generative modules. Analogous to fractalsarxiv.org한국 시간으로 25일에 최초로 나온 따끈따끈한 논문을 들고 왔다. 웬만하면 이런 짧은 제목의 논문은 잘 보지 않는 편인데, MASK R-CNN, ResNet, Focal Loss, FPN 등의 .. 2025. 2. 26. [Paper Review] StyleGAN : A Style-Based Generator Architecture for Generative Adversarial Networks A Style-Based Generator Architecture for Generative Adversarial NetworksWe propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identitarxiv.org* 이 논문 리뷰는 StyleGAN 시리즈 논문에 대한 세미나를 준비하면서 작성된 글이기에, 다소 얕고 주인장.. 2025. 2. 25. 이전 1 다음 반응형
