and Xu C. Knowledge distillation from a stronger teacher. NIPS, 2022.概用Pearson correlation coefficient 来替代一般的 KL 散度用于蒸馏.DIST首先, 作者针对不同的 model size ResNet18, Resnet50 和不同的训练策略 B1, B2 (B2 更复杂一点, 通过 B2 训练得到的模型一般效果更好一点) 训练得到不同的教师...
文献(Chen, X., Su, J., & Zhang, J. (2019b). A two-teacher tramework for knowledge distillation. In ISNN.)使用了两个教师网络,其中一名教师将基于响应的知识迁移给学生,另一名将基于特征的知识迁移给学生。文献(Fukuda, T., Suzuki, M., Kurata, G., Thomas, S., Cui, J. & Ramabhadran,B...
Paper name: Knowledge Distillation with the Reused Teacher Classifier Publication: CVPR2022 Problem: Knowledge Distillation Abstract: 知识蒸馏旨在将强大而繁琐的教师模型压缩成轻量级的学生模型,而不会牺牲太多性能。为此,过去几年提出了各种方法,通常采用精心设计的知识表示,这反过来又增加了模型开发和解释的难度。相...
而student学习迁移来自teacher的监督信息的过程称之为Distillation(蒸馏)。
an Ensemble of Self-Supervised Teacher models for obtaining a Minimal Student Model (ESTMS), focusing on efficient knowledge transfer through distillation. Th... J Kishore,S Mukherjee - 《Progress in Artificial Intelligence》 被引量: 0发表: 2024年 Multi-exit self-distillation with appropriate teac...
论文中一些关于 distillation 的相关结论: 数据增强要保证 teacher(T), student(S) 两者一致。 训练epoch 要增大,文中的直接用的 10k epoch,没有发生 overfit(没有 overfit,那就是 deep learning 的天堂)。 可以用高清输入的 T 来训练 S,但是看文中的精度,提升性价比不高,要消耗太多算力。
As a popular compression method, knowledge distillation transfers knowledge from a large (teacher) model to a small (student) one. However, existing methods perform distillation on the entire data, which easily leads to repetitive learning for the student. Furthermore, the capacity gap between the...
As a representative type of model compression and acceleration, knowledge distillation effectively learns a small student model from a large teacher model. It has received rapid increasing attention from the community. This paper provides a comprehensive survey of knowledge distillation from the ...
SimKD 的一个关键组成部分是“分类器重用”操作,即我们直接借用预训练的教师分类器进行学生推理,而不是利用教师网络的soft target去训练一个新的分类器。这消除了标签信息计算交叉熵损失的需要,并使特征对齐损失成为生成梯度的唯一来源。 来自预训练教师模型的重用部分允许合并更多层,而不仅仅是仅限于最终分类器。通常...
Teacher-Tutor-Student Knowledge Distillation is a method for image virtual try-on models. It treats fake images produced by the parser-based method as "tutor knowledge", where the artifacts can be corrected by real "teacher knowledge", which is extracted from the real person images in a self...