This paper analyzes the characteristics of emotion state and group behavior in the evacuation process.During the emergency evacuation,emotion state and group behavior are interacting with each other,and indivisible.Th...This paper analyzes the characteristics of emotion state and group behavior in the evacuation process.During the emergency evacuation,emotion state and group behavior are interacting with each other,and indivisible.The emotion spread model with the effect of group behavior,and the leader-follower model with the effect of emotion state are proposed.On this basis,exit choice strategies with the effect of emotion state and group behavior are proposed.Fusing emotion spread model,leader-follower model,and exit choice strategies into a cellular automata(CA)-based pedestrian simulation model,we simulate the evacuation process in a multi-exit case.Simulation results indicate that panic emotion and group behavior are two negative influence factors for pedestrian evacuation.Compared with panic emotion or group behavior only,pedestrian evacuation efficiency with the effects of both is lower.展开更多
Multi-exit architecture allows early-stop inference to reduce computational cost,which can be used in resource-constrained circumstances.Recent works combine the multi-exit architecture with self-distillation to simul...Multi-exit architecture allows early-stop inference to reduce computational cost,which can be used in resource-constrained circumstances.Recent works combine the multi-exit architecture with self-distillation to simultaneously achieve high efficiency and decent performance at different network depths.However,existing methods mainly transfer knowledge from deep exits or a single ensemble to guide all exits,without considering that inappropriate learning gaps between students and teachers may degrade the model performance,especially in shallow exits.To address this issue,we propose Multi-exit self-distillation with Appropriate TEachers(MATE)to provide diverse and appropriate teacher knowledge for each exit.In MATE,multiple ensemble teachers are obtained from all exits with different trainable weights.Each exit subsequently receives knowledge from all teachers,while focusing mainly on its primary teacher to keep an appropriate gap for efficient knowledge transfer.In this way,MATE achieves diversity in knowledge distillation while ensuring learning efficiency.Experimental results on CIFAR-100,TinyImageNet,and three fine-grained datasets demonstrate that MATE consistently outperforms state-of-the-art multi-exit self-distillation methods with various network architectures.展开更多
基金Project supported by the National Key Research and Development Program of China(Grant No.2017YFC0803903)the National Natural Science Foundation of China(Grant No.62003182)。
文摘This paper analyzes the characteristics of emotion state and group behavior in the evacuation process.During the emergency evacuation,emotion state and group behavior are interacting with each other,and indivisible.The emotion spread model with the effect of group behavior,and the leader-follower model with the effect of emotion state are proposed.On this basis,exit choice strategies with the effect of emotion state and group behavior are proposed.Fusing emotion spread model,leader-follower model,and exit choice strategies into a cellular automata(CA)-based pedestrian simulation model,we simulate the evacuation process in a multi-exit case.Simulation results indicate that panic emotion and group behavior are two negative influence factors for pedestrian evacuation.Compared with panic emotion or group behavior only,pedestrian evacuation efficiency with the effects of both is lower.
基金supported by the National Natural Science Foundation of China(No.U1866602)the Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study,China(No.SN-ZJU-SIAS-001)。
文摘Multi-exit architecture allows early-stop inference to reduce computational cost,which can be used in resource-constrained circumstances.Recent works combine the multi-exit architecture with self-distillation to simultaneously achieve high efficiency and decent performance at different network depths.However,existing methods mainly transfer knowledge from deep exits or a single ensemble to guide all exits,without considering that inappropriate learning gaps between students and teachers may degrade the model performance,especially in shallow exits.To address this issue,we propose Multi-exit self-distillation with Appropriate TEachers(MATE)to provide diverse and appropriate teacher knowledge for each exit.In MATE,multiple ensemble teachers are obtained from all exits with different trainable weights.Each exit subsequently receives knowledge from all teachers,while focusing mainly on its primary teacher to keep an appropriate gap for efficient knowledge transfer.In this way,MATE achieves diversity in knowledge distillation while ensuring learning efficiency.Experimental results on CIFAR-100,TinyImageNet,and three fine-grained datasets demonstrate that MATE consistently outperforms state-of-the-art multi-exit self-distillation methods with various network architectures.
