CV149 [2025-1] 임재열- DRÆM – A discriminatively trained reconstruction embedding for surface anomaly detection DRAEM은 2021 ICCV에서 발표된 복원-원본 이미지 쌍을 활용해 anomaly detection을 학습하는 새로운 unsupervised 모델을 제안하는 논문입니다. [DRAEM]https://arxiv.org/abs/2108.07610 DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectionVisual surface anomaly detection aims to detect local image regions that significantly deviate from normal appearance. Recent surface anomaly detection methods rely on .. 2025. 5. 17. [2025-1] 유경석 - FlexiViT: One Model for All Patch Sizes https://arxiv.org/pdf/2212.08013https://github.com/google-research/big_vision GitHub - google-research/big_vision: Official codebase used to develop Vision Transformer, SigLIP, MLP-Mixer, LiT and more.Official codebase used to develop Vision Transformer, SigLIP, MLP-Mixer, LiT and more. - google-research/big_visiongithub.comAbstractViT의 patch size는 speed와 accuracy를 결정하는 인자이지만, patch size를 변경하는 것.. 2025. 5. 17. [2025-1] 전연주 - VAE: Auto-Encoding Variational Bayes 논문 링크: 1312.6114코드 링크: 2025-OUTTA-Gen-AI/Reviews/Diffusion/Auto-Encoding Variational Bayes.ipynb at 1b4ef8a85c6d5b0d0cacea47ed0ef1a39f843be7 · youngunghan/2025-OUTTA-Gen-AI1. Introduction 연속적인 latent variable 또는 parameter를 포함한 directed probabilistic model(방향성을 갖는 확률 그래프 모델로 latent variable z와 observed data x 사이의 관계를 정의 → generative process를 모델링하는 방식)에서는, 특정 관측값에 대한 posterior 분포 $p(z \mid x)$를 계산.. 2025. 5. 17. [2025-1] 김유현 - Progressive Growing of GAN https://arxiv.org/abs/1710.10196 Progressive Growing of GANs for Improved Quality, Stability, and VariationWe describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training prograrxiv.org 0. Abstract논문에서는 Prgressi.. 2025. 5. 17. 이전 1 2 3 4 5 6 7 8 ··· 38 다음
