SJLeo/FFSD

Pytorch implementation of our paper accepted by IEEE TNNLS, 2022 -- Distilling a Powerful Student Model via Online Knowledge Distillation

Distilling a Powerful Student Model via Online Knowledge Distillation (Link).

The framework of our proposed FFSD for online knowledge distillation. First, student 1 and student 2 learn from each other in a collaborative way. Then by shifting the attention of student 1 and distilling it to student 2, we are able to enhance the diversity among students. Last, the feature fusion module fuses all the students’ information into a fused feature map. The fused representation is then used to assist the learning of the student leader. After training, we simply adopt the student leader which achieves superior performance over all other students.

Getting Started

The code has been tested using Pytorch1.5.1 and CUDA10.2 on Ubuntu 18.04.

Please type the command

pip install -r requirements.txt

to install dependencies.

FFSD

• You can run the following code to train models on CIFAR-100:

  python cifar.py
  	--dataroot ./database/cifar100
  	--dataset cifar100
  	--model resnet32
  	--lambda_diversity 1e-5
  	--lambda_self_distillation 1000
  	--lambda_fusion 10
  	--gpu_ids 0
  	--name cifar100_resnet32_div1e-5_sd1000_fusion10

• You can run the following code to train models on ImageNet:

  python distribute_imagenet.py
  	--dataroot ./database/imagenet
  	--dataset imagenet
  	--model resnet18
  	--lambda_diversity 1e-5
  	--lambda_self_distillation 1000
  	--lambda_fusion 10
  	--gpu_ids 0,1
  	--name imagenet_resnet18_div1e-5_sd1000_fusion10

Experimental Results

We provide the student leader models in the experiments, along with their training loggers and configurations.

Model	Dataset	Top1 Accuracy (%)	Download
ResNet20	CIFAR-100	72.64	Link
ResNet20	CIFAR-100	72.58	Link
ResNet20	CIFAR-100	72.88	Link
ResNet32	CIFAR-100	74.92	Link
ResNet32	CIFAR-100	74.82	Link
ResNet32	CIFAR-100	74.82	Link
ResNet56	CIFAR-100	75.84	Link
ResNet56	CIFAR-100	75.66	Link
ResNet56	CIFAR-100	75.91	Link
WRN-16-2	CIFAR-100	75.87	Link
WRN-16-2	CIFAR-100	75.86	Link
WRN-16-2	CIFAR-100	75.69	Link
WRN-40-2	CIFAR-100	79.13	Link
WRN-40-2	CIFAR-100	79.19	Link
WRN-40-2	CIFAR-100	79.11	Link
DenseNet	CIFAR-100	77.29	Link
DenseNet	CIFAR-100	77.70	Link
DenseNet	CIFAR-100	77.17	Link
GoogLeNet	CIFAR-100	81.52	Link
GoogLeNet	CIFAR-100	81.93	Link
GoogLeNet	CIFAR-100	81.34	Link
ResNet-18	ImageNet	70.87	Link
ResNet-34	ImageNet	74.69	Link

You can use the following code to test our models.

python test.py
	--dataroot ./database/cifar100
	--dataset cifar100
	--model resnet32
	--gpu_ids 0
	--load_path ./resnet32/cifar100_resnet32_div1e-5_sd1000_fusion10_1/modelleader_best.pth

Tips

Any problem, free to contact the authors via emails:shaojieli@stu.xmu.edu.cn.