Awesome

Information Theoretic Representation Distillation

Code for the BMVC2022 paper:

"Information Theoretic Representation Distillation".
Roy Miles, Adrian Lopez Rodriguez, Krystian Mikolajczyk. BMVC 2022.

[Paper on arxiv]

Running

The provided code is for reproducing the CIFAR100 experimental results in our paper Information Theoretic Representation Distillation . The code is tested with Python 3.8.

Fetch the pretrained teacher models by:

sh scripts/fetch_pretrained_teachers.sh

which will download and save the models to save/models

Install dependancies:

conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
pip install tensorboard_logger

An example of performing distillation with ITRD losses is given as follows:

python train_student.py --path_t ./save/models/wrn_40_2_vanilla/ckpt_epoch_240.pth --model_s wrn_16_2 -b 1.0 -r 1.0 --lambda_corr 2.0 --lambda_mutual 1.0 --alpha_it 1.01

where the flags are explained as:

--path_t: specify the path of the teacher model
--model_s: specify the student model, see 'models/init.py' to check the available model types.
--b: the weight of other distillation losses, default: None
--r: the weight of the cross-entropy loss between logit and ground truth, default: 1
--lambda_corr: Correlation loss weighting
--lambda_mutual: Mutual information loss weighting
--alpha_it: Renyi's alpha-order used in the correlation loss

Benchmark Results on CIFAR-100:

Performance is measured by classification accuracy (%)

Teacher and student are of the same architectural type.

Teacher <br> Student	wrn-40-2 <br> wrn-16-2	wrn-40-2 <br> wrn-40-1	resnet56 <br> resnet20	resnet110 <br> resnet20	resnet110 <br> resnet32	resnet32x4 <br> resnet8x4	vgg13 <br> vgg8
Teacher <br> Student	75.61 <br> 73.26	75.61 <br> 71.98	72.34 <br> 69.06	74.31 <br> 69.06	74.31 <br> 71.14	79.42 <br> 72.50	74.64 <br> 70.36
KD	74.92	73.54	70.66	70.67	73.08	73.33	72.98
FitNet	73.58	72.24	69.21	68.99	71.06	73.50	71.02
AT	74.08	72.77	70.55	70.22	72.31	73.44	71.43
SP	73.83	72.43	69.67	70.04	72.69	72.94	72.68
CC	73.56	72.21	69.63	69.48	71.48	72.97	70.71
VID	74.11	73.30	70.38	70.16	72.61	73.09	71.23
RKD	73.35	72.22	69.61	69.25	71.82	71.90	71.48
PKT	74.54	73.45	70.34	70.25	72.61	73.64	72.88
AB	72.50	72.38	69.47	69.53	70.98	73.17	70.94
FT	73.25	71.59	69.84	70.22	72.37	72.86	70.58
FSP	72.91	N/A	69.95	70.11	71.89	72.62	70.23
NST	73.68	72.24	69.60	69.53	71.96	73.30	71.53
CRD	75.48	74.14	71.16	71.46	73.48	75.51	73.94
WCoRD	76.11	74.72	71.92	71.88	74.20	76.15	74.72
ReviewKD	76.12	75.09	71.89	-	73.89	75.63	74.85
ITRD	76.12	75.18	71.47	71.99	74.26	76.69	74.93

Teacher and student are of different architectural type.

Teacher <br> Student	vgg13 <br> MobileNetV2	ResNet50 <br> MobileNetV2	ResNet50 <br> vgg8	resnet32x4 <br> ShuffleNetV1	resnet32x4 <br> ShuffleNetV2	wrn-40-2 <br> ShuffleNetV1
Teacher <br> Student	74.64 <br> 64.60	79.34 <br> 64.60	79.34 <br> 70.36	79.42 <br> 70.50	79.42 <br> 71.82	75.61 <br> 70.50
KD	67.37	67.35	73.81	74.07	74.45	74.83
FitNet	64.14	63.16	70.69	73.59	73.54	73.73
AT	59.40	58.58	71.84	71.73	72.73	73.32
SP	66.30	68.08	73.34	73.48	74.56	74.52
CC	64.86	65.43	70.25	71.14	71.29	71.38
VID	65.56	67.57	70.30	73.38	73.40	73.61
RKD	64.52	64.43	71.50	72.28	73.21	72.21
PKT	67.13	66.52	73.01	74.10	74.69	73.89
AB	66.06	67.20	70.65	73.55	74.31	73.34
FT	61.78	60.99	70.29	71.75	72.50	72.03
NST	58.16	64.96	71.28	74.12	74.68	74.89
CRD	69.73	69.11	74.30	75.11	75.65	76.05
WCoRD	70.02	70.12	74.68	75.77	76.48	76.68
ReviewKD	70.37	69.89	-	77.45	77.78	77.14
ITRD	70.39	71.34	75.49	76.69	77.40	77.09

Binary Distillation

Code can be found at: https://drive.google.com/file/d/1WJ_rGIsQ-SaqvXzNsfkxcNA6rWBbcnD8/view?usp=sharing Relevant comments: https://github.com/roymiles/ITRD/issues/1

Citation

@inproceedings{miles2022itrd,
  title={Information Theoretic Representation Distillation},
  author={Miles, Roy and Lopez-Rodriguez, Adrian and Mikolajczyk, Krystian},
  booktitle={British Machine Vision Conference (BMVC)},
  year={2022}
}

Acknowledgements

Our code is based on the code given in RepDistiller.