평범한 필기장

Paper : https://arxiv.org/abs/2412.15205 FlowAR: Scale-wise Autoregressive Image Generation Meets Flow MatchingAutoregressive (AR) modeling has achieved remarkable success in natural language processing by enabling models to generate text with coherence and contextual understanding through next token prediction. Recently, in image generation, VAR proposes scale-wisarxiv.orgAbstract기존 VAR은 다음 두가지..

Paper : https://arxiv.org/abs/2502.09616 Variational Rectified Flow MatchingWe study Variational Rectified Flow Matching, a framework that enhances classic rectified flow matching by modeling multi-modal velocity vector-fields. At inference time, classic rectified flow matching 'moves' samples from a source distribution to the tararxiv.orgAbstract 본 논문은 multi-modal velocity vector-fields를 모델링함으로..

Paper : https://arxiv.org/abs/2210.02747 Flow Matching for Generative ModelingWe introduce a new paradigm for generative modeling built on Continuous Normalizing Flows (CNFs), allowing us to train CNFs at unprecedented scale. Specifically, we present the notion of Flow Matching (FM), a simulation-free approach for training CNFs basearxiv.org(성민혁 교수님 강의 자료 참고 : https://www.youtube.com/watch?v=B4F..