给定一个三元组,在teacher某一层输出中存在与anchor point的距离排序关系,其思想为降低positive样本和anchor之间距离,拉大anchor和negative之间的距离,而student也是为了学习teacher输出的这个三元组相对差异关系。 【Adaptive Multi-Teacher Multi-level Knowledge Distillation】 暂不展开 三、保留多teacher的多样性 【Ensembl...
To address this limitation, we propose BOMD (Bi-level Optimization for Multi-teacher Distillation), a novel approach that combines bi-level optimization with multiple orthogonal projections. Our method employs orthogonal projections to align teacher feature representations with the student's feat...
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...
1、多个教师模型(大模型)教一个学生模型(小模型),避免单个教师模型教学生模型,导致bias 2、当有多个老师时候,学生模型可否根据自己能力和教师特点,选择性进行学习。(假设学生很聪明) 3、最好的老师不一定教出最好的学生,比如Roberta模型>bert,但是两个模型的学生模型性能却是bert高于Roberta。原因很好理解,大模型善...
In this paper, we propose a multi-teacher distillation (MTD) method for the incremental learning of industrial detectors. Our proposed method leverages structural similarity loss to identify the most representative data, enhancing the efficiency of the incremental learning process. Additionally, we ...
MTDP: a Multi-Teacher Distillation approach for Protein embedding aims to enhance efficiency while preserving high-resolution representations. By leveraging the knowledge of multiple pre-trained protein embedding models, MTDP learns a compact and informative representation of proteins....
Class Incremental Learning with Multi-Teacher Distillation Haitao Wen, Lili Pan, Yu Dai, Heqian Qiu, Lanxiao Wang*, Qingbo Wu, Hongliang Li* University of Electronic Science and Technology of China, Chengdu, China {haitaowen, ydai, lanxiao.wang}@std.uestc.edu.cn, {lili...
To address these issues, we design a medical image unsupervised domain adaptation segmentation model, UDA-FMTD, based on Fourier feature decoupling and multi-teacher distillation. Evaluations conducted on the MICCAI 2017 MM-WHS cardiac dataset have demonstrated the effectiveness and superiority of this ...
在知识蒸馏的对抗教师和正常教师(clean teacher)的指导下,希望学生能够从对抗教师那里学习鲁棒性,并从干净教师那里学习识别正常样本的能力。 为了产生履行两位教师责任的软标签,正常教师的输入是来自原始数据集的初始正常样本。相反,对抗教师的输入是学生模型在内部最大化中产生的对抗样本。 学生输入分为正常样本和对抗样...
我们提出了一种新颖的对抗性鲁棒性蒸馏方法,称为多教师对抗性鲁棒性蒸馏(MTARD),该方法应用多个教师模型通过对抗性蒸馏来提高学生模型的干净和鲁棒准确性。 我们设计了一个基于所提出的自适应规范化损失的联合训练算法,以平衡对抗性教师模型和干净教师模型对学生模型的影响,这种影响是由历史训练信息动态决定的。 我们通...