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Attention Transfer

PyTorch code for "Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer"
The paper is under review as a conference submission at ICLR2017:

What's in this repo so far:

  • Activation-based AT code for CIFAR-10 experiments
  • Code for ImageNet experiments (ResNet-18-ResNet-34 student-teacher)


  • grad-based AT
  • Scenes and CUB activation-based AT code
  • Pretrained with activation-based AT ResNet-18

The code uses PyTorch Note that the original experiments were done using torch-autograd, we have so far validated that CIFAR-10 experiments are exactly reproducible in PyTorch, and are in process of doing so for ImageNet (results are very slightly worse in PyTorch, due to hyperparameters).


    author = {Sergey Zagoruyko and Nikos Komodakis},
    title = {Paying More Attention to Attention: Improving the Performance of
             Convolutional Neural Networks via Attention Transfer},
    url = {},
    year = {2016}}


First install PyTorch, then install torchnet:

git clone
cd tnt
python install

Install OpenCV with Python bindings, and torchvision with OpenCV transforms:

git clone
cd vision; git checkout opencv
python install

Finally, install other Python packages:

pip install -r requirements.txt



This section describes how to get the results in the table 1 of the paper.

First, train teachers:

python --save logs/resnet_40_1_teacher --depth 40 --width 1
python --save logs/resnet_16_2_teacher --depth 16 --width 2
python --save logs/resnet_40_2_teacher --depth 40 --width 2

To train with activation-based AT do:

python --save logs/at_16_1_16_2 --teacher_id resnet_16_2_teacher --beta 1e+3

To train with KD:

python --save logs/kd_16_1_16_2 --teacher_id resnet_16_2_teacher --alpha 0.9

We plan to add AT+KD with decaying beta to get the best knowledge transfer results soon.


Pretrained model

We provide ResNet-18 pretrained model with activation based AT:

Model val error
ResNet-18 30.4, 10.8
ResNet-18-ResNet-34-AT 29.3, 10.0

Download link: [coming]

Model definition: [coming]

Convergence plot:

Train from scratch

Download pretrained weights for ResNet-34 (see also functional-zoo for more information):


Prepare the data following fb.resnet.torch and run training (e.g. using 2 GPUs):

python --imagenetpath ~/ILSVRC2012 --depth 18 --width 1 \
                   --teacher_params resnet-34-export.hkl --gpu_id 0,1 --ngpu 2 \
                   --beta 1e+3