분류 전체보기271 [2024-1] 김경훈 - MUNIT(Multi-Modal Unsupervised Image-to-Image translation) 원본 논문 링크 : https://arxiv.org/abs/1804.04732 Multimodal Unsupervised Image-to-Image Translation Unsupervised image-to-image translation is an important and challenging problem in computer vision. Given an image in the source domain, the goal is to learn the conditional distribution of corresponding images in the target domain, without seeing any pair arxiv.org 깃허브 https://github.com/NVlabs/MUNIT .. 2024. 3. 26. [2024-1] 김동한 - Nonparametric statistical tests for the continuous data: the basic concept and the practical use https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4754273/ Nonparametric statistical tests for the continuous data: the basic concept and the practical use Conventional statistical tests are usually called parametric tests. Parametric tests are used more frequently than nonparametric tests in many medical articles, because most of the medical researchers are familiar with and the statistical software.. 2024. 3. 25. [2024-1] 백승우 - Denoising Diffusion Probabilistic Models Denoising Diffusion Probabilistic Models We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound arxiv.org 0. Abstract 확산 확률 모델과 랑게빈 역학과의 노이즈 제거 점수 매칭 사이의 새로운 연결에 따라 설계된 가중 가변 바운드에 대한 훈련을 통해 최상의 결과.. 2024. 3. 20. [2024-1] 양소정 - How transferable are features in deep neural networks? https://arxiv.org/abs/1411.1792 How transferable are features in deep neural networks? Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Such first-layer features appear not to be specific to a particular dataset or task arxiv.org general/specific의 관념적 정의 첫 번째 레이어에서 standard.. 2024. 3. 19. 이전 1 ··· 54 55 56 57 58 59 60 ··· 68 다음
