Semi-supervised sound event detection(SSED)tasks typically leverage a large amount of unlabeled and synthetic data to facilitate model generalization during training,reducing overfitting on a limited set of labeled da...Semi-supervised sound event detection(SSED)tasks typically leverage a large amount of unlabeled and synthetic data to facilitate model generalization during training,reducing overfitting on a limited set of labeled data.However,the generalization training process often encounters challenges from noisy interference introduced by pseudo-labels or domain knowledge gaps.To alleviate noisy interference in class distribution learning,we propose an efficient semi-supervised class distribution learning method through dynamic prompt tuning,named prompting class distribution optimization(PADO).Specifically,when modeling real labeled data,PADO dynamically incorporates independent learnable prompt tokens to explore prior knowledge about the true distribution.Then,the prior knowledge serves as prompt information,dynamically interacting with the posterior noisy-class distribution information.In this case,PADO achieves class distribution optimization while maintaining model generalization,leading to a significant improvement in the efficiency of class distribution learning.Compared with state-of-the-art methods on the SSED datasets from DCASE 2019,2020,and 2021 challenges,PADO achieves significant performance improvements.Furthermore,it is readily extendable to other benchmark models.展开更多
在规定的培养条件下,细菌的细胞尺寸大小能稳定在一定的范围内,即便遭受环境波动或自身内部噪声影响,细菌细胞仍能够维持自身尺寸稳态.这种尺寸调控遵循怎样的规律,以及规律背后的生物学意义如何,一直以来都吸引着人们关注,相关研究也...在规定的培养条件下,细菌的细胞尺寸大小能稳定在一定的范围内,即便遭受环境波动或自身内部噪声影响,细菌细胞仍能够维持自身尺寸稳态.这种尺寸调控遵循怎样的规律,以及规律背后的生物学意义如何,一直以来都吸引着人们关注,相关研究也在进行.近年来,由于群体水平的研究并不能很好地解释一些现象,在单细胞水平探究细菌生长过程所遵循的各种规律成为一种新的趋势.得益于多种单细菌捕获技术的发展,研究者们能够更加方便、合理地在单细胞水平对细菌开展相关研究.对细菌的尺寸调控研究,单细胞数据所呈现的结果与群体研究所得到的信息存在出入,前者能更精准、全面地反映被群体所覆盖的信息,使我们从群体培养所得到的“筛选器(sizer)”与“计时器(timer)”模型转向“加法器(adder)”与“噪声线性谱(noisy linear map)”模型.本文将结合单细菌捕获技术的介绍,简述细菌的各种尺寸调控机制理论与模型,希望有助于研究者快速了解本领域目前的研究进展与相关的基础理论体系.展开更多
基金supported by the National Natural Science Foundation of China(Nos.62176106 and U1836220)the Special Scientific Research Project of School of Emergency Management of Jiangsu University(No.KY-A-01)+2 种基金the Project of Faculty of Agricultural Engineering of Jiangsu University(No.NGXB20240101)the Post-graduate Research&Practice Innovation Program of Jiangsu Province(Nos.KYCX22_3668 and KYCX21_3373)the Jiangsu Key Research and Development Plan(No.BE2020036)。
文摘Semi-supervised sound event detection(SSED)tasks typically leverage a large amount of unlabeled and synthetic data to facilitate model generalization during training,reducing overfitting on a limited set of labeled data.However,the generalization training process often encounters challenges from noisy interference introduced by pseudo-labels or domain knowledge gaps.To alleviate noisy interference in class distribution learning,we propose an efficient semi-supervised class distribution learning method through dynamic prompt tuning,named prompting class distribution optimization(PADO).Specifically,when modeling real labeled data,PADO dynamically incorporates independent learnable prompt tokens to explore prior knowledge about the true distribution.Then,the prior knowledge serves as prompt information,dynamically interacting with the posterior noisy-class distribution information.In this case,PADO achieves class distribution optimization while maintaining model generalization,leading to a significant improvement in the efficiency of class distribution learning.Compared with state-of-the-art methods on the SSED datasets from DCASE 2019,2020,and 2021 challenges,PADO achieves significant performance improvements.Furthermore,it is readily extendable to other benchmark models.
文摘在规定的培养条件下,细菌的细胞尺寸大小能稳定在一定的范围内,即便遭受环境波动或自身内部噪声影响,细菌细胞仍能够维持自身尺寸稳态.这种尺寸调控遵循怎样的规律,以及规律背后的生物学意义如何,一直以来都吸引着人们关注,相关研究也在进行.近年来,由于群体水平的研究并不能很好地解释一些现象,在单细胞水平探究细菌生长过程所遵循的各种规律成为一种新的趋势.得益于多种单细菌捕获技术的发展,研究者们能够更加方便、合理地在单细胞水平对细菌开展相关研究.对细菌的尺寸调控研究,单细胞数据所呈现的结果与群体研究所得到的信息存在出入,前者能更精准、全面地反映被群体所覆盖的信息,使我们从群体培养所得到的“筛选器(sizer)”与“计时器(timer)”模型转向“加法器(adder)”与“噪声线性谱(noisy linear map)”模型.本文将结合单细菌捕获技术的介绍,简述细菌的各种尺寸调控机制理论与模型,希望有助于研究者快速了解本领域目前的研究进展与相关的基础理论体系.
