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【论文笔记】(防御蒸馏)Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
有关蒸馏 Distillation 的论文 xff1a 2006 Model Compression 2014 Do Deep Nets Really Need to be Deep 论文笔记 2015 Distilling the Kno
Distillation
Defense
Adversarial
Perturbations
Against
Mosaicking to Distill Knowledge Distillation from Out-of-Domain Data
Mosaicking to Distill Knowledge Distillation from Out of Domain Data 在本文中 xff0c 我们试图解决一项雄心勃勃的任务 xff0c 即域外知识蒸馏 xff08 OOD
Mosaicking
Distill
Knowledge
Distillation
from
CONTRASTIVE REPRESENTATION DISTILLATION
CONTRASTIVE REPRESENTATION DISTILLATION 我们常常希望将表征性知识从一个神经网络转移到另一个神经网络 这方面的例子包括将一个大型网络提炼成一个较小的网络 xff0c 将知识从一种感觉模式转移到另一种感觉
Contrastive
Representation
Distillation
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data, NeurIPS 2021
motivation STARTUP ICLR2021 中提出基于self training的思想用target domain的去标记数据联合训练模型 但STARTUP中使用在base classes上预先训练得到的网络 xff0c 为未标
Dynamic
Distillation
network
for
cross
《Channel-wise Knowledge Distillation for Dense Prediction》论文详解
原文地址 xff1a Channel wise Knowledge Distillation for Dense Prediction 代码地址 xff1a https git io Distille xff08 由原文提供 xff0c 好
channel
wise
Knowledge
Distillation
for
扩散模型相关论文阅读,扩散模型和知识蒸馏的结合提升预测速度:Progressive Distillation for Fast Sampling of Diffusion Models
目录 论文地址及代码速览主要解决的问题 扩散模型预测慢 0 Abstruct0 1 逐句翻译总结 1 INTRODUCTION1 1逐句翻译第一段 xff08 扩散模型在各个方面取得很好的成果 xff09 第二段 xff08 提出扩散模型预
Progressive
Distillation
for
Fast
sampling