분류 전체보기295 [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]박제우 - Scaling Language-Image Pre-training via Masking https://arxiv.org/abs/2212.00794 Scaling Language-Image Pre-training via MaskingWe present Fast Language-Image Pre-training (FLIP), a simple and more efficient method for training CLIP. Our method randomly masks out and removes a large portion of image patches during training. Masking allows us to learn from more image-text pairs givearxiv.org https://blog.outta.ai/284 본 논문은 지난번 리뷰했던 자연어 지도 학습 모.. 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 ··· 74 다음
