Computer Vision127 [2024-1] 양소정 - Generative Adversarial Networks https://arxiv.org/abs/1406.2661 Generative Adversarial NetworksWe propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability thatarxiv.org Abstract적대적(adversarial) 프로세스를 통해 생성 모델을 추정하는 프레임워크 제안함이 프레임워크는 'minim.. 2024. 4. 10. [2024-1] 백승우 - You Only Watch Once: A Unified CNN Architecturefor Real-Time Spatiotemporal Action Localization You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action Localization Spatiotemporal action localization requires the incorporation of two sources of information into the designed architecture: (1) temporal information from the previous frames and (2) spatial information from the key frame. Current state-of-the-art approache arxiv.org 0. Abstract Spatiotemporal action .. 2024. 4. 4. [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] 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. 이전 1 ··· 25 26 27 28 29 30 31 32 다음
