Results 11 to 20 of about 142,324 (226)
Feature fusion-based collaborative learning for knowledge distillation
International Journal of Distributed Sensor Networks, 2021 Deep neural networks have achieved a great success in a variety of applications, such as self-driving cars and intelligent robotics. Meanwhile, knowledge distillation has received increasing attention as an effective model compression technique for ...
Yiting Li +4 more
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Knowledge distillation in deep learning and its applications [PDF]
PeerJ Computer Science, 2021 Deep learning based models are relatively large, and it is hard to deploy such models on resource-limited devices such as mobile phones and embedded devices.
Abdolmaged Alkhulaifi +2 more
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Reverse Self-Distillation Overcoming the Self-Distillation Barrier
IEEE Open Journal of the Computer Society, 2023 Deep neural networks generally cannot gather more helpful information with limited data in image classification, resulting in poor performance. Self-distillation, as a novel knowledge distillation technique, integrates the roles of teacher and student ...
Shuiping Ni +4 more
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Multi-assistant Dynamic Setting Method for Knowledge Distillation [PDF]
Jisuanji kexueKnowledge distillation is increasingly gaining attention in key areas such as model compression for object recognition.Through in-depth research into the efficiency of knowledge distillation and an analysis of the characteristics of knowledge transfer ...
SI Yuehang, CHENG Qing, HUANG Jincai
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A novel model compression method based on joint distillation for deepfake video detection
Journal of King Saud University: Computer and Information Sciences, 2023 In recent years, deepfake videos have been abused to create fake news, which threaten the integrity of digital videos. Although existing detection methods leveraged cumbersome neural networks to achieve promising detection performance, they cannot be ...
Xiong Xu +5 more
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Forest Fire Object Detection Analysis Based on Knowledge Distillation
Fire, 2023 This paper investigates the application of the YOLOv7 object detection model combined with knowledge distillation techniques in forest fire detection.
Jinzhou Xie, Hongmin Zhao
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Sub-Band Knowledge Distillation Framework for Speech Enhancement
, 2020 In single-channel speech enhancement, methods based on full-band spectral features have been widely studied. However, only a few methods pay attention to non-full-band spectral features.
Gao, Guanglai +5 more
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Discriminator-Enhanced Knowledge-Distillation Networks
Applied Sciences, 2023 Query auto-completion (QAC) serves as a critical functionality in contemporary textual search systems by generating real-time query completion suggestions based on a user’s input prefix. Despite the prevalent use of language models (LMs) in QAC candidate
Zhenping Li +4 more
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A non-negative feedback self-distillation method for salient object detection [PDF]
PeerJ Computer Science, 2023 Self-distillation methods utilize Kullback-Leibler divergence (KL) loss to transfer the knowledge from the network itself, which can improve the model performance without increasing computational resources and complexity. However, when applied to salient
Lei Chen +6 more
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Feature Fusion for Online Mutual Knowledge Distillation
, 2020 We propose a learning framework named Feature Fusion Learning (FFL) that efficiently trains a powerful classifier through a fusion module which combines the feature maps generated from parallel neural networks. Specifically, we train a number of parallel
Chung, Inseop +3 more
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