Despite the fact that deep neural networks are powerful models and achieve appealing results on many tasks, they are too large to be deployed on edge devices like smartphones or embedded sensor nodes. There have been efforts to compress these networks, and a popular method is knowledge distillation, where a large (teacher) pre-trained network is used to train a smaller (student) network. However, in this paper, we show that the student network performance degrades when the gap between student and teacher is large. Given a fixed student network, one cannot employ an arbitrarily large teacher, or in other words, a teacher can effectively transfer its knowledge to students up to a certain size, not smaller. To alleviate this shortcoming, we in...
Deep learning is used for automatic modulation recognition in neural networks, and because of the ne...
Deep neural networks have achieved a great success in a variety of applications, such as self-drivin...
In recent years, deep neural networks have been successful in both industry and academia, especially...
Despite the fact that deep neural networks are powerful models and achieve appealing results on many...
Deep neural networks (DNNs) have achieved great success in various machine learning tasks. However, ...
Knowledge distillation compacts deep networks by letting a small student network learn from a large ...
One of the main problems in the field of Artificial Intelligence is the efficiency of neural network...
Knowledge distillation compacts deep networks by letting a small student network learn from a large ...
Deep neural networks have exhibited state-of-the-art performance in many com- puter vision tasks. H...
This paper presents a novel framework for facilitating communication and knowledge exchange among ne...
Although deep neural networks have enjoyed remarkable success across a wide variety of tasks, their ...
Although Deep neural networks (DNNs) have shown a strong capacity to solve large-scale problems in m...
학위논문 (석사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2018. 8. 조성준.Deep neural networks have showed success in many ar...
Knowledge distillation (KD) is an effective tool for compressing deep classification models for edge...
Transferring knowledge from a teacher neural network pretrained on the same or a similar task to a s...
Deep learning is used for automatic modulation recognition in neural networks, and because of the ne...
Deep neural networks have achieved a great success in a variety of applications, such as self-drivin...
In recent years, deep neural networks have been successful in both industry and academia, especially...
Despite the fact that deep neural networks are powerful models and achieve appealing results on many...
Deep neural networks (DNNs) have achieved great success in various machine learning tasks. However, ...
Knowledge distillation compacts deep networks by letting a small student network learn from a large ...
One of the main problems in the field of Artificial Intelligence is the efficiency of neural network...
Knowledge distillation compacts deep networks by letting a small student network learn from a large ...
Deep neural networks have exhibited state-of-the-art performance in many com- puter vision tasks. H...
This paper presents a novel framework for facilitating communication and knowledge exchange among ne...
Although deep neural networks have enjoyed remarkable success across a wide variety of tasks, their ...
Although Deep neural networks (DNNs) have shown a strong capacity to solve large-scale problems in m...
학위논문 (석사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2018. 8. 조성준.Deep neural networks have showed success in many ar...
Knowledge distillation (KD) is an effective tool for compressing deep classification models for edge...
Transferring knowledge from a teacher neural network pretrained on the same or a similar task to a s...
Deep learning is used for automatic modulation recognition in neural networks, and because of the ne...
Deep neural networks have achieved a great success in a variety of applications, such as self-drivin...
In recent years, deep neural networks have been successful in both industry and academia, especially...