device=torch.device('cuda'iftorch.cuda.is_available()else'cpu')teacher=Teacher(3,8,0.01).to(device)train_teacher(teacher,trainloader,epochs=20) student=Student(3,8,0.01).to(device)train_student(teacher,student,trainloader,epochs=20)evaluate(student,testloader) 从测试结果上看,确实不是模型越大...
The proposed AdaDistill embeds the KD concept into the softmax loss by training the student using a margin penalty softmax loss with distilled class centers from the teacher. Being aware of the relatively low capacity of the compact student model, we propose to distill less complex knowledge at...
Knowledge Transfer (KT), which aims at training a smaller student network by transferring knowledge from a larger teacher model, is one of the popular solutions. In this paper, we propose a novel knowledge transfer method by treating it as a distribution matching problem. Particularly, we match...
We introduce three different mechanisms of knowledge transfer in the propsoed MOMA framework. : (1) Distill pre-trained MoCo to MAE. (2) Distill pre-trained MAE to MoCo (3) Distill pre-trained MoCo and MAE to a random initialized student. During the distillation, the teacher and the ...
Tutorial: Knowledge Distillation 概述Knowledge Distillation(KD)一般指利用一个大的teacher网络作为监督,帮助一个小的student网络进行学习,主要用于模型压缩。 其方法主要分为两大类 Output Distillation Feature Distillation Output Distillation Motivation 主要拉近teacher和student最终输出的距离,参考论文:Dis... ...
Paper:Distilling Knowledge From Graph Convolutional Networks, CVPR'20 Method Overview Dependencies PyTorch = 1.1.0 DGL = 1.4.0 Seerequirmentfile for more information about how to install the dependencies. Themain.pyfile contains the code for training teacher model, training the student model using ...
Publication|Publication Knowledge distillation (KD) is widely used for compressing a teacher model to a smaller student model, reducing its inference cost and memory footprint while preserving model capabilities. However, current KD methods for auto-regressive sequence models (e...
BERT-PKD (Patient Knowledge Distillation) 在hinton提到两个损失之上,再加上一个loss:L_PT。 PKD论文中做了对比,减少模型宽度和减少模型深度,得到的结论是减少宽度带来的efficiency提高不如减少深度来的更大。 论文所提出的多层蒸馏,即Student模型除了学习Teacher模型的概率输出之外,还要学习一些中间层的输出。论文提出...
This is the official implementation ofUniDistill(CVPR2023 highlight✨, 10% of accepted papers). UniDistill offers a universal cross-modality knowledge distillation framework for different teacher and student modality combinations. The core idea is aligning the intermediate BEV features and response feat...
在Adapt-then-Distill部分,验证了之前研究的的结论:好的teacher模型可以产生好的student模型。采用大模型中表现最好的AdaLM模型作为teacher,在生物医学领域和计算机科学领域取得了很好的结果,优于其他特定领域的大模型。 此外,我们发现一个更好的初始化student模型也有助于得到一个更好的小模型。在Adapt-and-Distill部分...