전체 글378 [2026-1] 백승우 - Self-Improving Pretraining:using post-trained models to pretrain better models Self-Improving Pretraining: using post-trained models to pretrain better modelsEnsuring safety, factuality and overall quality in the generations of large language models is a critical challenge, especially as these models are increasingly deployed in real-world applications. The prevailing approach to addressing these issues involvearxiv.org 2026. 2. 4. [2026-1] 이루가 - High-Resolution Image Synthesis with Latent Diffusion Models 논문 링크: 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 확산모델(Diffusion)의 고성능은 유지하면서 계산비용.. 2026. 1. 31. [2025-2] 김효민 - U-Net: Convolutional Networks for Biomedical Image Segmentation 본 글에서는 U-Net의 구조를 상세히 다루고 있으며, U-Net++에 대해서도 간단히 다루고 있다.본 글은 U-Net 논문과 기타 기술블로그를 참고하여 작성한 글이다. 다음은 U-Net 논문의 링크이다. 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 augmenta.. 2026. 1. 31. [2026-1] 박승원 - SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS Paper Information- 논문링크: https://arxiv.org/pdf/1609.02907- 게재 컨퍼런스 : International Conference on Learning Representations(ICLR) 2017- 저자: *Thomas N. Kipf, Max Welling from University of Amsterdam그래프들어가기에 앞서 그래프가 생소한 분들을 위해 개념을 소개합니다.그래프란? 객체들의 집합이며, 객체의 일부는 서로 관련이 있음. 그래프는 정점(Vertices, Nodes)와 그 정점들을 잇는 간선(Edges)으로 구성됨. 객체 간 관계를 모델링할 때 그래프를 사용함."A graph is a structure consisting of a set of obje.. 2026. 1. 30. 이전 1 2 3 4 ··· 95 다음
