양자화 썸네일형 리스트형 [CVPR 2025] Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers https://arxiv.org/abs/2406.17343 , CVPR 2025 (Accepted) Q-DiT: Accurate Post-Training Quantization for Diffusion TransformersRecent advancements in diffusion models, particularly the architectural transformation from UNet-based models to Diffusion Transformers (DiTs), significantly improve the quality and scalability of image and video generation. However, despite their impressiarxiv.org Abstrac.. 더보기 [Low-power Computer Vision 2022] A Survey of Quantization Methods for Efficient Neural Network Inference https://arxiv.org/abs/2103.13630Low-power Computer Vision, 2022 A Survey of Quantization Methods for Efficient Neural Network InferencThis chapter provides approaches to the problem of quantizing the numerical values in deep Neural Network computations, covering the advantages/disadvantages ofwww.taylorfrancis.comAbstract AI분야에서 신경망 모델의 성능발전으로 인해 메모리 및 computational resource 관련 한계가 발생하고 있음.해당 한계.. 더보기 이전 1 다음
