Continual learning(CL)studies the problem of learning to accumulate knowledge over time from a stream of data.A crucial challenge is that neural networks suffer from performance degradation on previously seen data,kno...Continual learning(CL)studies the problem of learning to accumulate knowledge over time from a stream of data.A crucial challenge is that neural networks suffer from performance degradation on previously seen data,known as catastrophic forgetting,due to allowing parameter sharing.In this work,we consider a more practical online class-incremental CL setting,where the model learns new samples in an online manner and may continuously experience new classes.Moreover,prior knowledge is unavailable during training and evaluation.Existing works usually explore sample usages from a single dimension,which ignores a lot of valuable supervisory information.To better tackle the setting,we propose a novel replay-based CL method,which leverages multi-level representations produced by the intermediate process of training samples for replay and strengthens supervision to consolidate previous knowledge.Specifically,besides the previous raw samples,we store the corresponding logits and features in the memory.Furthermore,to imitate the prediction of the past model,we construct extra constraints by leveraging multi-level information stored in the memory.With the same number of samples for replay,our method can use more past knowledge to prevent interference.We conduct extensive evaluations on several popular CL datasets,and experiments show that our method consistently outperforms state-of-the-art methods with various sizes of episodic memory.We further provide a detailed analysis of these results and demonstrate that our method is more viable in practical scenarios.展开更多
Climate change poses significant challenges to agricultural management,particularly in adapting to extreme weather conditions that impact agricultural production.Existing works with traditional Reinforcement Learning(...Climate change poses significant challenges to agricultural management,particularly in adapting to extreme weather conditions that impact agricultural production.Existing works with traditional Reinforcement Learning(RL)methods often falter under such extreme conditions.To address this challenge,our study introduces a novel approach by integrating Continual Learning(CL)with RL to form Continual Reinforcement Learning(CRL),enhancing the adaptability of agricultural management strategies.Leveraging the Gym-DSSAT simulation environment,our research enables RL agents to learn optimal fertilization strategies based on variable weather conditions.By incorporating CL algorithms,such as Elastic Weight Consolidation(EWC),with established RL techniques like Deep Q-Networks(DQN),we developed a framework in which agents can learn and retain knowledge across diverse weather scenarios.The CRL approach was tested under climate variability to assess the robustness and adaptability of the induced policies,particularly under extreme weather events like severe droughts.Our results showed that continually learned policies exhibited superior adaptability and performance compared to optimal policies learned through the conventional RL methods,especially in challenging conditions of reduced rainfall and increased temperatures.This pioneering work,which combines CL with RL to generate adaptive policies for agricultural management,is expected to make significant advancements in precision agriculture in the era of climate change.展开更多
As a data-driven approach, Deep Learning(DL)-based fault diagnosis methods need to collect the relatively comprehensive data on machine fault types to achieve satisfactory performance. A mechanical system may include ...As a data-driven approach, Deep Learning(DL)-based fault diagnosis methods need to collect the relatively comprehensive data on machine fault types to achieve satisfactory performance. A mechanical system may include multiple submachines in the real-world. During condition monitoring of a mechanical system, fault data are distributed in a continuous flow of constantly generated information and new faults will inevitably occur in unconsidered submachines, which are also called machine increments. Therefore, adequately collecting fault data in advance is difficult. Limited by the characteristics of DL, training existing models directly with new fault data of new submachines leads to catastrophic forgetting of old tasks, while the cost of collecting all known data to retrain the models is excessively high. DL-based fault diagnosis methods cannot learn continually and adaptively in dynamic environments. A new Continual Learning Fault Diagnosis method(CLFD) is proposed in this paper to solve a series of fault diagnosis tasks with machine increments. The stability–plasticity dilemma is an intrinsic issue in continual learning. The core of CLFD is the proposed Dual-branch Adaptive Aggregation Residual Network(DAARN).Two types of residual blocks are created in each block layer of DAARN: steady and dynamic blocks. The stability–plasticity dilemma is solved by assigning them with adaptive aggregation weights to balance stability and plasticity, and a bi-level optimization program is used to optimize adaptive aggregation weights and model parameters. In addition, a feature-level knowledge distillation loss function is proposed to further overcome catastrophic forgetting. CLFD is then applied to the fault diagnosis case with machine increments. Results demonstrate that CLFD outperforms other continual learning methods and has satisfactory robustness.展开更多
Although modulation classification based on deep neural network can achieve high Modulation Classification(MC)accuracies,catastrophic forgetting will occur when the neural network model continues to learn new tasks.In...Although modulation classification based on deep neural network can achieve high Modulation Classification(MC)accuracies,catastrophic forgetting will occur when the neural network model continues to learn new tasks.In this paper,we simulate the dynamic wireless communication environment and focus on breaking the learning paradigm of isolated automatic MC.We innovate a research algorithm for continuous automatic MC.Firstly,a memory for storing representative old task modulation signals is built,which is employed to limit the gradient update direction of new tasks in the continuous learning stage to ensure that the loss of old tasks is also in a downward trend.Secondly,in order to better simulate the dynamic wireless communication environment,we employ the mini-batch gradient algorithm which is more suitable for continuous learning.Finally,the signal in the memory can be replayed to further strengthen the characteristics of the old task signal in the model.Simulation results verify the effectiveness of the method.展开更多
The automatic colorization of anime line drawings is a challenging problem in production pipelines.Recent advances in deep neural networks have addressed this problem;however,collectingmany images of colorization targ...The automatic colorization of anime line drawings is a challenging problem in production pipelines.Recent advances in deep neural networks have addressed this problem;however,collectingmany images of colorization targets in novel anime work before the colorization process starts leads to chicken-and-egg problems and has become an obstacle to using them in production pipelines.To overcome this obstacle,we propose a new patch-based learning method for few-shot anime-style colorization.The learning method adopts an efficient patch sampling technique with position embedding according to the characteristics of anime line drawings.We also present a continuous learning strategy that continuously updates our colorization model using new samples colorized by human artists.The advantage of our method is that it can learn our colorization model from scratch or pre-trained weights using only a few pre-and post-colorized line drawings that are created by artists in their usual colorization work.Therefore,our method can be easily incorporated within existing production pipelines.We quantitatively demonstrate that our colorizationmethod outperforms state-of-the-art methods.展开更多
Objectives： To analyse motivation and preferences of pharmacists who participate in CE （continuing education） to develop suitable lifelong learning programmes for pharmacists. Methods： An online questionnaire, whi...Objectives： To analyse motivation and preferences of pharmacists who participate in CE （continuing education） to develop suitable lifelong learning programmes for pharmacists. Methods： An online questionnaire, which explored the motivation and preferences of the pharmacists to lifelong learning, was sent to all members of the Royal Dutch Pharmaceutical Society （4321） in the Netherlands. The data were analysed using a non-hierarchical clustering technique. Key findings： Two clusters of pharmacists were discovered. Cluster A pharmacists （n = 474） were more motivated by credit points （63.5% vs. 47.2%）, personal interest （84.1% vs. 56.3%）, updating knowledge （73.8% vs. 56.8%） and topicality of CE courses （47.7% vs. 26.1%）. Cluster B pharmacists （n = 199） were predominantly motivated by the aspect ＂duty as a care-giver＂ （97.0% vs. 0 % in cluster A）. Pharmacists who belonged to cluster A tended to be women （60.5%）, often worked part-time （29.3%） and mostly preferred lectures （71.1%）. Cluster B pharmacists consisted of statistically significantly more male pharmacists （52.8%, p = 0.001）, worked more full time （77.4%, p = 0.009） and mostly preferred blended learning （62.3%, p = 0.047）. Conclusions： These results suggest the use of different education formats for different kinds of pharmacists to participate in CE activities.展开更多
Modelling,predicting and analysing driver behaviours are essential to advanced driver assistance systems(ADAS)and the comprehensive understanding of complex driving scenarios.Recently,with the development of deep lear...Modelling,predicting and analysing driver behaviours are essential to advanced driver assistance systems(ADAS)and the comprehensive understanding of complex driving scenarios.Recently,with the development of deep learning(DL),numerous driver behaviour learning(DBL)methods have been proposed and applied in connected vehicles(CV)and intelligent transportation systems(ITS).This study provides a review of DBL,which mainly focuses on typical applications in CV and ITS.First,a comprehensive review of the state-of-the-art DBL is presented.Next,Given the constantly changing nature of real driving scenarios,most existing learning-based models may suffer from the so-called“catastrophic forgetting,”which refers to their inability to perform well in previously learned scenarios after acquiring new ones.As a solution to the aforementioned issue,this paper presents a framework for continual driver behaviour learning(CDBL)by leveraging continual learning technology.The proposed CDBL framework is demonstrated to outperform existing methods in behaviour prediction through a case study.Finally,future works,potential challenges and emerging trends in this area are highlighted.展开更多
Purpose:During Japan's initial pandemic prevention and control period,the Ministry of Education,Culture,Sports,Science,and Technology of Japan(MEXT)issued several notifications to ensure students'continuous le...Purpose:During Japan's initial pandemic prevention and control period,the Ministry of Education,Culture,Sports,Science,and Technology of Japan(MEXT)issued several notifications to ensure students'continuous learning according to the"leave no one behind"philosophy.This study focused on the comprehensive measures comprising top-level arrangements to implement the"suspending classes without stopping learning"initiative.Design/Approach/Methods:The study reviewed MEXT's guide on ensuring learning for Japanese primary and secondary school students during the pandemic.Findings:The pandemic has accelerated the development of the information and communications technology educational environment and the implementation of the Global and Innovation Gateway for All(GIGA)schools initiative.These developments laid the foundation for the evolution of diverse pedagogical models and feasible methods for promoting equity.The home schooling experience during the pandemic provided a natural base for students to practice and foster survivability.Originality/Value:This paper provides scholars with an understanding of the opportunities and challenges encountered in educational innovation in Japan.It provides insights into the future direction of pedagogical development,capability development,and effective educational arrangements for extraordinary circumstances to facilitate educational development from a broader perspective,particularly under the new normal.展开更多
The power market is a typical imperfectly competitive market where power suppliers gain higher profits through strategic bidding behaviors.Most existing studies assume that a power supplier is accessible to the suffic...The power market is a typical imperfectly competitive market where power suppliers gain higher profits through strategic bidding behaviors.Most existing studies assume that a power supplier is accessible to the sufficient market information to derive an optimal bidding strategy.However,this assumption may not be true in reality,particularly when a power market is newly launched.To help power suppliers bid with the limited information,a modified continuous action reinforcement learning automata algorithm is proposed.This algorithm introduces the discretization and Dyna structure into continuous action reinforcement learning automata algorithm for easy implementation in a repeated game.Simulation results verify the effectiveness of the proposed learning algorithm.展开更多
基金supported in part by the National Natura Science Foundation of China(U2013602,61876181,51521003)the Nationa Key R&D Program of China(2020YFB13134)+2 种基金Shenzhen Science and Technology Research and Development Foundation(JCYJ20190813171009236)Beijing Nova Program of Science and Technology(Z191100001119043)the Youth Innovation Promotion Association,Chinese Academy of Sciences。
文摘Continual learning(CL)studies the problem of learning to accumulate knowledge over time from a stream of data.A crucial challenge is that neural networks suffer from performance degradation on previously seen data,known as catastrophic forgetting,due to allowing parameter sharing.In this work,we consider a more practical online class-incremental CL setting,where the model learns new samples in an online manner and may continuously experience new classes.Moreover,prior knowledge is unavailable during training and evaluation.Existing works usually explore sample usages from a single dimension,which ignores a lot of valuable supervisory information.To better tackle the setting,we propose a novel replay-based CL method,which leverages multi-level representations produced by the intermediate process of training samples for replay and strengthens supervision to consolidate previous knowledge.Specifically,besides the previous raw samples,we store the corresponding logits and features in the memory.Furthermore,to imitate the prediction of the past model,we construct extra constraints by leveraging multi-level information stored in the memory.With the same number of samples for replay,our method can use more past knowledge to prevent interference.We conduct extensive evaluations on several popular CL datasets,and experiments show that our method consistently outperforms state-of-the-art methods with various sizes of episodic memory.We further provide a detailed analysis of these results and demonstrate that our method is more viable in practical scenarios.
基金support from the University of Iowa OVPR Interdisciplinary Scholars Program and the US Department of Education(ED#P116S210005)for this study.Kishlay Jha’s work is supported in part by the US National Institute of Health(NIH)and National Science Foundation(NSF)under grants R01LM014012-01A1 and ITE-2333740.
文摘Climate change poses significant challenges to agricultural management,particularly in adapting to extreme weather conditions that impact agricultural production.Existing works with traditional Reinforcement Learning(RL)methods often falter under such extreme conditions.To address this challenge,our study introduces a novel approach by integrating Continual Learning(CL)with RL to form Continual Reinforcement Learning(CRL),enhancing the adaptability of agricultural management strategies.Leveraging the Gym-DSSAT simulation environment,our research enables RL agents to learn optimal fertilization strategies based on variable weather conditions.By incorporating CL algorithms,such as Elastic Weight Consolidation(EWC),with established RL techniques like Deep Q-Networks(DQN),we developed a framework in which agents can learn and retain knowledge across diverse weather scenarios.The CRL approach was tested under climate variability to assess the robustness and adaptability of the induced policies,particularly under extreme weather events like severe droughts.Our results showed that continually learned policies exhibited superior adaptability and performance compared to optimal policies learned through the conventional RL methods,especially in challenging conditions of reduced rainfall and increased temperatures.This pioneering work,which combines CL with RL to generate adaptive policies for agricultural management,is expected to make significant advancements in precision agriculture in the era of climate change.
基金supported by the National Natural Science Foundation of China(Nos.52272440,51875375)the China Postdoctoral Science Foundation Funded Project(No.2021M701503).
文摘As a data-driven approach, Deep Learning(DL)-based fault diagnosis methods need to collect the relatively comprehensive data on machine fault types to achieve satisfactory performance. A mechanical system may include multiple submachines in the real-world. During condition monitoring of a mechanical system, fault data are distributed in a continuous flow of constantly generated information and new faults will inevitably occur in unconsidered submachines, which are also called machine increments. Therefore, adequately collecting fault data in advance is difficult. Limited by the characteristics of DL, training existing models directly with new fault data of new submachines leads to catastrophic forgetting of old tasks, while the cost of collecting all known data to retrain the models is excessively high. DL-based fault diagnosis methods cannot learn continually and adaptively in dynamic environments. A new Continual Learning Fault Diagnosis method(CLFD) is proposed in this paper to solve a series of fault diagnosis tasks with machine increments. The stability–plasticity dilemma is an intrinsic issue in continual learning. The core of CLFD is the proposed Dual-branch Adaptive Aggregation Residual Network(DAARN).Two types of residual blocks are created in each block layer of DAARN: steady and dynamic blocks. The stability–plasticity dilemma is solved by assigning them with adaptive aggregation weights to balance stability and plasticity, and a bi-level optimization program is used to optimize adaptive aggregation weights and model parameters. In addition, a feature-level knowledge distillation loss function is proposed to further overcome catastrophic forgetting. CLFD is then applied to the fault diagnosis case with machine increments. Results demonstrate that CLFD outperforms other continual learning methods and has satisfactory robustness.
文摘Although modulation classification based on deep neural network can achieve high Modulation Classification(MC)accuracies,catastrophic forgetting will occur when the neural network model continues to learn new tasks.In this paper,we simulate the dynamic wireless communication environment and focus on breaking the learning paradigm of isolated automatic MC.We innovate a research algorithm for continuous automatic MC.Firstly,a memory for storing representative old task modulation signals is built,which is employed to limit the gradient update direction of new tasks in the continuous learning stage to ensure that the loss of old tasks is also in a downward trend.Secondly,in order to better simulate the dynamic wireless communication environment,we employ the mini-batch gradient algorithm which is more suitable for continuous learning.Finally,the signal in the memory can be replayed to further strengthen the characteristics of the old task signal in the model.Simulation results verify the effectiveness of the method.
文摘The automatic colorization of anime line drawings is a challenging problem in production pipelines.Recent advances in deep neural networks have addressed this problem;however,collectingmany images of colorization targets in novel anime work before the colorization process starts leads to chicken-and-egg problems and has become an obstacle to using them in production pipelines.To overcome this obstacle,we propose a new patch-based learning method for few-shot anime-style colorization.The learning method adopts an efficient patch sampling technique with position embedding according to the characteristics of anime line drawings.We also present a continuous learning strategy that continuously updates our colorization model using new samples colorized by human artists.The advantage of our method is that it can learn our colorization model from scratch or pre-trained weights using only a few pre-and post-colorized line drawings that are created by artists in their usual colorization work.Therefore,our method can be easily incorporated within existing production pipelines.We quantitatively demonstrate that our colorizationmethod outperforms state-of-the-art methods.
文摘Objectives： To analyse motivation and preferences of pharmacists who participate in CE （continuing education） to develop suitable lifelong learning programmes for pharmacists. Methods： An online questionnaire, which explored the motivation and preferences of the pharmacists to lifelong learning, was sent to all members of the Royal Dutch Pharmaceutical Society （4321） in the Netherlands. The data were analysed using a non-hierarchical clustering technique. Key findings： Two clusters of pharmacists were discovered. Cluster A pharmacists （n = 474） were more motivated by credit points （63.5% vs. 47.2%）, personal interest （84.1% vs. 56.3%）, updating knowledge （73.8% vs. 56.8%） and topicality of CE courses （47.7% vs. 26.1%）. Cluster B pharmacists （n = 199） were predominantly motivated by the aspect ＂duty as a care-giver＂ （97.0% vs. 0 % in cluster A）. Pharmacists who belonged to cluster A tended to be women （60.5%）, often worked part-time （29.3%） and mostly preferred lectures （71.1%）. Cluster B pharmacists consisted of statistically significantly more male pharmacists （52.8%, p = 0.001）, worked more full time （77.4%, p = 0.009） and mostly preferred blended learning （62.3%, p = 0.047）. Conclusions： These results suggest the use of different education formats for different kinds of pharmacists to participate in CE activities.
基金Supported by the National Key Research and Development Program of China(No.2022ZD0115503).
文摘Modelling,predicting and analysing driver behaviours are essential to advanced driver assistance systems(ADAS)and the comprehensive understanding of complex driving scenarios.Recently,with the development of deep learning(DL),numerous driver behaviour learning(DBL)methods have been proposed and applied in connected vehicles(CV)and intelligent transportation systems(ITS).This study provides a review of DBL,which mainly focuses on typical applications in CV and ITS.First,a comprehensive review of the state-of-the-art DBL is presented.Next,Given the constantly changing nature of real driving scenarios,most existing learning-based models may suffer from the so-called“catastrophic forgetting,”which refers to their inability to perform well in previously learned scenarios after acquiring new ones.As a solution to the aforementioned issue,this paper presents a framework for continual driver behaviour learning(CDBL)by leveraging continual learning technology.The proposed CDBL framework is demonstrated to outperform existing methods in behaviour prediction through a case study.Finally,future works,potential challenges and emerging trends in this area are highlighted.
基金supported by the Chinese Ministry of Education Project for the Key Research Institute of Humanities and Social Sciences at Universities,entitled"Cross-Boundary Curriculum Partnerships Between Schooling and Shadow Education"(Project number:22JJD880028).
文摘Purpose:During Japan's initial pandemic prevention and control period,the Ministry of Education,Culture,Sports,Science,and Technology of Japan(MEXT)issued several notifications to ensure students'continuous learning according to the"leave no one behind"philosophy.This study focused on the comprehensive measures comprising top-level arrangements to implement the"suspending classes without stopping learning"initiative.Design/Approach/Methods:The study reviewed MEXT's guide on ensuring learning for Japanese primary and secondary school students during the pandemic.Findings:The pandemic has accelerated the development of the information and communications technology educational environment and the implementation of the Global and Innovation Gateway for All(GIGA)schools initiative.These developments laid the foundation for the evolution of diverse pedagogical models and feasible methods for promoting equity.The home schooling experience during the pandemic provided a natural base for students to practice and foster survivability.Originality/Value:This paper provides scholars with an understanding of the opportunities and challenges encountered in educational innovation in Japan.It provides insights into the future direction of pedagogical development,capability development,and effective educational arrangements for extraordinary circumstances to facilitate educational development from a broader perspective,particularly under the new normal.
基金This work was supported by the National Natural Science Foundation of China(No.U1866206).
文摘The power market is a typical imperfectly competitive market where power suppliers gain higher profits through strategic bidding behaviors.Most existing studies assume that a power supplier is accessible to the sufficient market information to derive an optimal bidding strategy.However,this assumption may not be true in reality,particularly when a power market is newly launched.To help power suppliers bid with the limited information,a modified continuous action reinforcement learning automata algorithm is proposed.This algorithm introduces the discretization and Dyna structure into continuous action reinforcement learning automata algorithm for easy implementation in a repeated game.Simulation results verify the effectiveness of the proposed learning algorithm.
