In this paper, we propose a novel Multi-Teacher Knowledge Distillation (MTKD) framework specifically for image super-resolution. It exploits the advantages of multiple teachers by combining and enhancing the outputs of these teacher models, which then guides the learning process of the compact ...
QA-Are bigger models better teachers? On the efficacy of knowledge distillation. ICCV 2019 模型容量不匹配,导致student模型不能够mimic teacher,反而带偏了主要的loss;KD losses 和accuracy不匹配,导致student虽然可以follow teacher, 但是并不能吸收teacher知识。 QA-Is a pretrained teacher important? Deep mutual...
We propose a multi-teacher knowledge distillation framework for compressed video action recognition to compress this model. With this framework, the model is compressed by transferring the knowledge from multiple teachers to a single small student model. With multi-teacher knowledge distillation, students...
联合训练的Paper地址:https://arxiv.org/abs/1711.05852 2、Exploring Knowledge Distillation of Deep Neural Networks for Efficient Hardware Solutions 这篇文章将total loss重新定义如下: GitHub地址:https://github.com/peterliht/knowledge-distillation-pytorch total loss的Pytorch代码如下,引入了精简网络输出与教师网...
In this paper, instead of training high-dimensional models, we propose MulDE, a novel knowledge distillation framework, which includes multiple low-dimensional hyperbolic KGE models as teachers and two student components, namely Junior and Senior. Under a novel iterative distillation strategy, the ...
3、Ensemble of Multiple Teachers Paper地址:Efficient Knowledge Distillation from an Ensemble of Teachers | Request PDF 第一种算法:多个教师网络输出的Soft label按加权组合,构成统一的Soft label,然后指导学生网络的训练: 第二种算法:由于加权平均方式会弱化、平滑多个教师网络的预测结果,因此可以随机选择某个教师...
Most pioneering studies either learn from only a single teacher in their distillation learning methods, neglecting the potential that a student can learn from multiple teachers simultaneously, or simply treat each teacher to be equally important, unable to reveal the different importance of teachers ...
Paper 7:《Densely Guided Knowledge Distillation using Multiple Teacher Assistants》 ICCV 2021 Highlight 本文关注的内容是,teacher和student的模型大小在差距很大的情况下,如何用多个teacher assistants来逐步指导student的学习。 如下图所示,本文的主要思路是,为了防止中间的某个助手模型崩了的话,对最终结果影线很大,...
The team propose team-knowledge distillation networks (TKD-Net) to tackle the CD-FSL, which explores a strategy to help the cooperation of multiple teachers. They distill knowledge from the cooperation of teacher networks to a single student network in a meta-learning framework. It incorporates ta...
3、Ensemble of Multiple Teachers 第一种算法:多个教师网络输出的soft label按加权组合,构成统一的soft label,然后指导学生网络的训练: 第二种算法:由于加权平均方式会弱化、平滑多个教师网络的预测结果,因此可以随机选择某个教师网络的soft label作为guidance: ...