知识蒸馏常见范式(选自https://nervanasystems.github.io/distiller/knowledge_distillation.html) 工作一:Data-Free Learning of Student Networks(arXiv:1904.01186v4) DAFL示意图 本方法涉及一些GAN的知识,可以简单看一下陈诚:通俗理解生成对抗网络GAN。其主要做法是,把训练好的模型(叫做老师模型)作为一种判别器,训练...
这种情况下,使用PTQ(训练后量化)是一种策略,还有一些策略也可以近似完成该任务,比如:无数据蒸馏。 Data-Free Knowledge Distillation for Deep Neural Networks 文章模型压缩pipeline:在所有数据上训好的teacher模型及部分数据metadata --> 使用metadata和模型重建数据集--&...
然而，无数据蒸馏(Data-Free Knowledge Distillation)作为一种革新策略，正试图突破这一局限。它的核心流程是通过教师模型和部分元数据，巧妙地重构数据集，然后让新模型从中学到知识。这种方法依赖于统计量，如顶层和所有层的激活统计，谱信息，以及dropout强化神经元间的联系，尽管实验中展现出一些创新的灵感...
To solve these problems, in this paper, we propose a data-free knowledge distillation method called DFPU, which introduce positive-unlabeled (PU) learning. For training a compact neural network without data, a generator is introduced to generate pseudo data under the supervision of the teacher ...
3. Data-Free Learning for Super-resolution To provide better compressed models while protecting user privacy, we propose a data-free knowledge distillation framework for super-resolution networks. 3.1. Training Samples Generation Let T be a pre-trained teacher super-resolution ...
Data-free Knowledge Distillation for Object Detection We present DeepInversion for Object Detection (DIODE) to enable data-free knowledge distillation for neural networks trained on the object detection task. ... A Chawla,H Yin,P Molchanov,... - Workshop on Applications of Computer Vision 被引量...
Data-Free Knowledge Distillationfor Deep Neural NetworksRaphael Gontijo Lopes Stefano Fenu Thad StarnerGeorgia Institute of Technology Georgia Institute of Technology Georgia Institute of TechnologyAbstractRecent advances in model compression haveprovided procedures for compressing largeneural networks to a fractio...
技术标签:data-free 查看原文 Knowledge Distillation for BERT Unsupervised Domain Adaptation 1、介绍KnowledgeDistillationfor BERT Unsupervised Domain Adaptation运用BERT模型和distillation技术实现源域到目标域的知识迁移,提出了一种简单而有效的无监督域适应方法,它将对抗性区分域适应(ADDA)框架与知识相结合蒸馏,在30个...
3. Method Our new data-free knowledge distillation framework con- sists of two steps: (i) model inversion, and (ii) application- specific knowledge distillation. In this section, we briefly discuss the background and notation, and then introduce our DeepInversion and Adaptive DeepInversion ...
we propose a data-free knowledge distillation} approach to address heterogeneous FL, where the server learns a lightweight generator to ensemble user information in a data-free manner, which is then broadcasted to users, regulating local training using the learned knowledge as an inductive bias. Em...