检索结果-南通市图书馆

Audio Mixing Inversion via Embodied self-supervised learning

Machine Intelligence Research 2024年第1期21卷 55-62页

作者： Haotian Zhou Feng Yu Xihong Wu Department of AI Music and Music Information Technology Central Conservatory of MusicBeijing 100031China Laboratory of Music Artificial Intelligence Laboratory of Philosophy and Social SciencesMinistry of EducationBeijing 100031China School of Intelligence Science and Technology Peking UniversityBeijing 100871China

Audio mixing is a crucial part of music production.For analyzing or recreating audio mixing,it is of great importance to conduct research on estimating mixing parameters used to create mixdowns from music recordings,i.e.,audio mixing inversion.However,approaches of audio mixing inversion are rarely explored.A method of estimating mixing parameters from raw tracks and a stereo mixdown via embodied self-supervised learning is presented.In this work,several commonly used audio effects including gain,pan,equalization,reverb,and compression,are taken into consideration.This method is able to learn an inference neural network that takes a stereo mixdown and the raw audio sources as input and estimate mixing parameters used to create the mixdown by iteratively sampling and training.During the sampling step,the inference network predicts a set of mixing parameters,which is sampled and fed to an audio-processing framework to generate audio data for the training step.During the training step,the same network used in the sampling step is optimized with the sampled data generated from the sampling step.This method is able to explicitly model the mixing process in an interpretable way instead of using a black-box neural network model.A set of objective measures are used for evaluation.The experimental results show that this method has better performance than current state-of-the-art methods.

关键词： Audio mixing inversion intelligent audio mixing self-supervised learning audio signal processing deep learning

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

self-supervised learning-based oil spill detection of hyperspectral images

引用

Science China(Technological Sciences) 2022年第4期65卷 793-801页

作者： DUAN PuHong XIE ZhuoJun KANG XuDong LI ShuTao College of Electrical and Information Engineering Hunan UniversityChangsha 410082China Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province Changsha 410082China

Oil spill monitoring in remote sensing field has become a very popular technology to detect the spatial distribution of polluted regions.However,previous studies mainly focus on the supervised detection technologies,which requires a large number of high-quality training set.To solve this problem,we propose a self-supervised learning method to learn the deep neural network from unlabelled hyperspectral data for oil spill detection,which consists of three parts:data augmentation,unsupervised deep feature learning,and oil spill detection network.First,the original image is augmented with spectral and spatial transformation to improve robustness of the self-supervised model.Then,the deep neural networks are trained on the augmented data without label information to produce the high-level semantic features.Finally,the pre-trained parameters are transferred to establish a neural network classifier to obtain the detection result,where a contrastive loss is developed to fine-tune the learned parameters so as to improve the generalization ability of the proposed method.Experiments performed on ten oil spill datasets reveal that the proposed method obtains promising detection performance with respect to other state-of-the-art hyperspectral detection approaches.

关键词： hyperspectral image self-supervised learning data augmentation oil spill detection contrastive loss

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Repeatable adaptive keypoint detection via self-supervised learning

引用

Science China(Information Sciences) 2022年第11期65卷 123-147页

作者： Pei YAN Yihua TAN Yuan TAI National Key Laboratory of Science & Technology on Multi-spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology

Keypoint-based matching is a fundamental technology for different computer vision tasks, in which keypoint detection is a crucial step and directly affects the entire performance. Based on deep learning approaches, the learning-based keypoint detectors have been significantly developed. To further improve the accuracy of high-level matching tasks, the extracted keypoints should provide more accurate point-to-point correspondences and maintain a uniform spatial distribution. Based on this idea, a self-supervised learning method of keypoint detection named repeatable adaptive point is proposed. This method consists of a selfsupervised objective and an optimization algorithm. The objective maximizes the repeatability measure with the sparsity constraint of keypoints. This sparsity constraint is formulated by combining the non-maximum suppression operation and the penalty function of keypoint number, which generally makes keypoints have a uniform spatial distribution. A novel approximate alternate optimization algorithm is proposed to maximize the above objective, whose convergence is proved in theory. The proposed detector is “adaptive” because the combinations of it and some existing descriptors can adapt to high-level matching tasks with a fast convergence speed. Specifically, the combinations of it and SuperPoint/HardNet descriptors achieve stateof-the-art accuracy on three high-level tasks based on image matching, namely homography estimation,camera pose estimation, and three-dimensional reconstruction. Furthermore, the proposed method converges faster on new scenes compared with the state-of-the-art method that jointly optimizes the detector and the descriptor.

关键词： keypoint detection convolutional neural network image matching self-supervised learning repeatable

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

What makes for uniformity for non-contrastive self-supervised learning?

引用

Science China(Technological Sciences) 2022年第10期65卷 2399-2408页

作者： WANG YinQuan ZHANG XiaoPeng TIAN Qi Lü JinHu Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Huawei Inc. Shenzhen 518128China School of Automation Science and Electrical Engineering State Key Laboratory of Software Development EnvironmentBeihang UniversityBeijing 100191China

Recent advances in self-supervised learning(SSL) have made remarkable progress, especially for contrastive methods that target pulling two augmented views of one image together and pushing the views of all other images away. In this setting, negative pairs play a key role in avoiding collapsed representation. Recent studies, such as those on bootstrap your own latent(BYOL)and SimSiam, have surprisingly achieved a comparable performance even without contrasting negative samples. However, a basic theoretical issue for SSL arises: how can different SSL methods avoid collapsed representation, and is there a common design principle? In this study, we look deep into current non-contrastive SSL methods and analyze the key factors that avoid collapses.To achieve this goal, we present a new indicator of uniformity metric and study the local dynamics of the indicator to diagnose collapses in different scenarios. Moreover, we present some principles for choosing a good predictor, such that we can explicitly control the optimization process. Our theoretical analysis result is validated on some widely used benchmarks spanning differentscale datasets. We also compare recent SSL methods and analyze their commonalities in avoiding collapses and some ideas for future algorithm designs.

关键词： contrastive learning self-supervised learning representation uniformity dynamics

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

self-supervised graph learning with target-adaptive masking for session-based recommendation

引用

Frontiers of Information Technology & Electronic Engineering 2023年第1期24卷 73-87页

作者： Yitong WANG Fei CAI Zhiqiang PAN Chengyu SONG Science and Technology on Information Systems Engineering Laboratory National University of Defense TechnologyChangsha 410073China

Session-based recommendation aims to predict the next item based on a user’s limited interactions within a short period.Existing approaches use mainly recurrent neural networks(RNNs)or graph neural networks(GNNs)to model the sequential patterns or the transition relationships between items.However,such models either ignore the over-smoothing issue of GNNs,or directly use cross-entropy loss with a softmax layer for model optimization,which easily results in the over-fitting problem.To tackle the above issues,we propose a self-supervised graph learning with target-adaptive masking(SGL-TM)method.Specifically,we first construct a global graph based on all involved sessions and subsequently capture the self-supervised signals from the global connections between items,which helps supervise the model in generating accurate representations of items in the ongoing session.After that,we calculate the main supervised loss by comparing the ground truth with the predicted scores of items adjusted by our designed target-adaptive masking module.Finally,we combine the main supervised component with the auxiliary self-supervision module to obtain the final loss for optimizing the model parameters.Extensive experimental results from two benchmark datasets,Gowalla and Diginetica,indicate that SGL-TM can outperform state-of-the-art baselines in terms of Recall@20 and MRR@20,especially in short sessions.

关键词： Session-based recommendation self-supervised learning Graph neural networks Target-adaptive

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Joint self-supervised and reference-guided learning for depth inpainting

引用

Computational Visual Media 2022年第4期8卷 597-612页

作者： Heng Wu Kui Fu Yifan Zhao Haokun Song Jia Li State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Depth information can benefit various computer vision tasks on both images and videos.However,depth maps may suffer from invalid values in many pixels,and also large holes.To improve such data,we propose a joint self-supervised and reference-guided learning approach for depth inpainting.For the self-supervised learning strategy,we introduce an improved spatial convolutional sparse coding module in which total variation regularization is employed to enhance the structural information while preserving edge information.This module alternately learns a convolutional dictionary and sparse coding from a corrupted depth map.Then,both the learned convolutional dictionary and sparse coding are convolved to yield an initial depth map,which is effectively smoothed using local contextual information.The reference-guided learning part is inspired by the fact that adjacent pixels with close colors in the RGB image tend to have similar depth values.We thus construct a hierarchical joint bilateral filter module using the corresponding color image to fill in large holes.In summary,our approach integrates a convolutional sparse coding module to preserve local contextual information and a hierarchical joint bilateral filter module for filling using specific adjacent information.Experimental results show that the proposed approach works well for both invalid value restoration and large hole inpainting.

关键词： depth inpainting self-supervised learning reference-guided learning

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Boosting battery state of health estimation based on self-supervised learning

引用

Journal of Energy Chemistry 2023年第9期84卷 335-346页

作者： Yunhong Che Yusheng Zheng Xin Sui Remus Teodorescu Department of Energy Aalborg UniversityAalborg 9220Denmark

State of health(SoH) estimation plays a key role in smart battery health prognostic and management.However,poor generalization,lack of labeled data,and unused measurements during aging are still major challenges to accurate SoH estimation.Toward this end,this paper proposes a self-supervised learning framework to boost the performance of battery SoH estimation.Different from traditional data-driven methods which rely on a considerable training dataset obtained from numerous battery cells,the proposed method achieves accurate and robust estimations using limited labeled data.A filter-based data preprocessing technique,which enables the extraction of partial capacity-voltage curves under dynamic charging profiles,is applied at first.Unsupervised learning is then used to learn the aging characteristics from the unlabeled data through an auto-encoder-decoder.The learned network parameters are transferred to the downstream SoH estimation task and are fine-tuned with very few sparsely labeled data,which boosts the performance of the estimation framework.The proposed method has been validated under different battery chemistries,formats,operating conditions,and ambient.The estimation accuracy can be guaranteed by using only three labeled data from the initial 20% life cycles,with overall errors less than 1.14% and error distribution of all testing scenarios maintaining less than 4%,and robustness increases with aging.Comparisons with other pure supervised machine learning methods demonstrate the superiority of the proposed method.This simple and data-efficient estimation framework is promising in real-world applications under a variety of scenarios.

关键词： Lithium-ion battery State of health Battery aging self-supervised learning Prognostics and health management Data-driven estimation

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Near real-time spatial prediction of earthquake-induced landslides:A novel interpretable self-supervised learning method

引用

International Journal of Digital Earth 2023年第1期16卷 1885-1906页

作者： Xuewen Wang Xianmin Wang Xinlong Zhang Lizhe Wang Haixiang Guo Dongdong Li Hubei Subsurface Multi-scale Imaging Key Laboratory School of Geophysics and GeomaticsChina University of GeosciencesWuhanPeople’s Republic of China State Key Laboratory of Biogeology and Environmental Geology China University of GeosciencesWuhanPeople’s Republic of China Key Laboratory of Geological and Evaluation of Ministry of Education China University of GeosciencesWuhanPeople’s Republic of China Laboratory of Natural Disaster Risk Prevension and Emergency Management School of Economics and ManagementChina University of GeosciencesWuhanPeople’s Republic of China College of Electronic Science and Engineering National University of Defense TechnologyChangshaPeople’s Republic of China

Near real-time spatial prediction of earthquake-induced landslides(EQILs)can rapidly forecast the occurrence position of widespread landslides just after a violent earthquake;thus,EQIL prediction is very crucial to the 72-hour‘golden window’for survivors.This work focuses on a series of earthquake events from 2008 to 2022 occurring in the Tibetan Plateau,a famous seismically-active zone,and proposes a novel interpretable self-supervised learning(ISeL)method for the near real-time spatial prediction of EQILs.This new method innovatively introduces swap noise at the unsupervised mechanism,which can improve the generalization performance and transferability of the model,and can effectively reduce false alarm and improve accuracy through supervisedfine-tuning.An interpretable module is built based on a self-attention mechanism to reveal the importance and contribution of various influencing factors to EQIL spatial distribution.Experimental results demonstrate that the ISeL model is superior to the excellent state-of-the-art machine learning and deep learning methods.Furthermore,according to the interpretable module in the ISeL method,the critical controlling and triggering factors are revealed.The ISeL method can also be applied in other earthquake-frequent regions worldwide because of its good generalization and transferability.

关键词： Coseismic landslide near real-time interpretable artificial intelligence self-supervised learning spatial distribution prediction

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

self-supervised Music Motion Synchronization learning for Music-Driven Conducting Motion Generation

引用

Journal of Computer Science & Technology 2022年第3期37卷 539-558页

作者：刘凡陈德龙周睿志杨赛许峰 College of Computer and Information Hohai UniversityNanjing 211100China School of Electrical Engineering Nantong UniversityNantong 226019China

The correlation between music and human motion has attracted widespread research attention.Although recent studies have successfully generated motion for singers,dancers,and musicians,few have explored motion generation for orchestral conductors.The generation of music-driven conducting motion should consider not only the basic music beats,but also mid-level music structures,high-level music semantic expressions,and hints for different parts of orchestras(strings,woodwind,etc.).However,most existing conducting motion generation methods rely heavily on human-designed rules,which significantly limits the quality of generated motion.Therefore,we propose a novel Music Motion Synchronized Generative Adversarial Network(M^(2)S-GAN),which generates motions according to the automatically learned music representations.More specifically,M^(2)S-GAN is a cross-modal generative network comprising four components:1)a music encoder that encodes the music signal;2)a generator that generates conducting motion from the music codes;3)a motion encoder that encodes the motion;4)a discriminator that differentiates the real and generated motions.These four components respectively imitate four key aspects of human conductors:understanding music,interpreting music,precision and elegance.The music and motion encoders are first jointly trained by a self-supervised contrastive loss,and can thus help to facilitate the music motion synchronization during the following adversarial learning process.To verify the effectiveness of our method,we construct a large-scale dataset,named ConductorMotion100,which consists of unprecedented 100 hours of conducting motion data.Extensive experiments on ConductorMotion100 demonstrate the effectiveness of M^(2)S-GAN.Our proposed approach outperforms various comparison methods both quantitatively and qualitatively.Through visualization,we show that our approach can generate plausible,diverse,and music-synchronized conducting motion.

关键词： self-supervised learning generative adversarial network human motion generation

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Contrastive self-supervised Representation learning Using Synthetic Data

引用

International Journal of Automation and computing 2021年第4期18卷 556-567页

作者： Dong-Yu She Kun Xu Beijing National Research Center for Information Science and Technology Department of Computer Science and TechnologyTsinghua UniversityBeijing 100084China

learning discriminative representations with deep neural networks often relies on massive labeled data, which is expensive and difficult to obtain in many real scenarios. As an alternative, self-supervised learning that leverages input itself as supervision is strongly preferred for its soaring performance on visual representation learning. This paper introduces a contrastive self-supervised framework for learning generalizable representations on the synthetic data that can be obtained easily with complete controllability.Specifically, we propose to optimize a contrastive learning task and a physical property prediction task simultaneously. Given the synthetic scene, the first task aims to maximize agreement between a pair of synthetic images generated by our proposed view sampling module, while the second task aims to predict three physical property maps, i.e., depth, instance contour maps, and surface normal maps. In addition, a feature-level domain adaptation technique with adversarial training is applied to reduce the domain difference between the realistic and the synthetic data. Experiments demonstrate that our proposed method achieves state-of-the-art performance on several visual recognition datasets.

关键词： self-supervised learning contrastive learning synthetic image convolutional neural network representation learning

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：