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...
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...
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...
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 ...
Adversarial Robustness Distillation (ARD)用于通过从大型鲁棒模型中提炼来提高小型模型的鲁棒性,它将大型模型视为教师,将小型模型视为学生。尽管之前的工作(RSLAD)通过鲁棒的软标签提高了鲁棒性,但与常规训练的性能相比,正常样本的准确性仍然不理想。 受多任务学习的启发,作者提出了多教师对抗鲁棒性蒸馏(MTARD),通过...
zhang等人尝试在鲁棒性和清洁性能之间取得平衡(TRADES),wang等人通过 Misclassification-Aware adversarial Training (MART) 进一步提高了性能。wu等人认为使用更大的模型可以提高模型的鲁棒性。 之前的工作使用了两个指标:干净准确率和鲁棒准确率,作为评估模型综合性能的度量。我们的实验中使用了Nezihe 等人提出的加权鲁棒...