Knowledge-Distillation-Zoo

News! I'm going to have a major update of this repo. The new version will contain most of the methods in Todo list. Please stay tuned.

Pytorch implementation of various Knowledge Distillation (KD) methods.

This repository is a simple reference, mainly focuses on basic knowledge distillation/transfer methods. Thus many tricks and variations, such as step-by-step training, iterative training, ensemble of teachers, ensemble of KD methods, data-free, self-distillation, online distillation etc. are not considered. Hope it is useful for your project or research.

I will update this repo regularly with new KD methods. If there some basic methods I missed, please contact with me.

Lists

Name	Method	Paper Link	Code Link
Baseline	basic model with softmax loss	—	code
Logits	mimic learning via regressing logits	paper	code
ST	soft target	paper	code
AT	attention transfer	paper	code
Fitnet	hints for thin deep nets	paper	code
NST	neural selective transfer	paper	code
PKT	probabilistic knowledge transfer	paper	code
FSP	flow of solution procedure	paper	code
FT	factor transfer	paper	code
RKD	relational knowledge distillation	paper	code
AB	activation boundary	paper	code
SP	similarity preservation	paper	code
Sobolev	sobolev/jacobian matching	paper	code
BSS	boundary supporting samples	paper	code
CC	correlation congruence	paper	code
LwM	learning without memorizing	paper	code
IRG	instance relationship graph	paper	code
VID	variational information distillation	paper	code
OFD	overhaul of feature distillation	paper	code
AFD	attention feature distillation	paper	code
CRD	contrastive representation distillation	paper	code
DML	deep mutual learning	paper	code

• Note, there are some differences between this repository and the original papers：
  • For AT: I use the sum of absolute values with power p=2 as the attention.
  • For Fitnet: The training procedure is one stage without hint layer.
  • For NST: I employ polynomial kernel with d=2 and c=0.
  • For AB: Two-stage training, the first 50 epochs for initialization, the second stage only employs CE without ST.
  • For BSS: 80% epochs employ CE+BSS loss, the rest 20% only uses CE. In addition, warmup for the first 10 epochs.
  • For CC: For consistency, I only consider CC without instance congruence. Gaussian RBF kernel is employed because Bilinear Pool kernel is similar with PKT. I choose P=2 order Taylor of Gaussian RBF kernel. No special sampling strategy.
  • For LwM: I employ it after rb2 (middle conv layer) but not rb3 (last conv layer), because the base net is resnet with the end of GAP followed by a classifier. If after rb3, the grad-CAN has the same values across H and W in each channel.
  • For IRG: I only use one-to-one mode.
  • For VID: I set the hidden channel size to be same with the output channel size and remove BN in μ.
  • For AFD: I find the original implementation of attention is unstable, thus replace it with a SE block.
  • For DML: Just two nets are employed. Synchronous update to avoid multiple forwards.

Datasets

• CIFAR10
• CIFAR100

Networks

• Resnet-20
• Resnet-110

The networks are same with Tabel 6 in paper.

Training

• Creating ./dataset directory and downloading CIFAR10/CIFAR100 in it.
• Using the script example_train_script.sh to train various KD methods. You can simply specify the hyper-parameters listed in train_xxx.py or manually change them.
• The hyper-parameters I used can be found in the training logs (code: ezed).
• Some Notes:
  • Sobolev/LwM alone is unstable and may be used in conjunction with other KD methods.
  • BSS may occasionally destroy the training procedure, leading to poor results.
  • If not specified in the original papers, all the methods can be used on the middle feature maps or multiple feature maps are only employed after the last conv layer. It is simple to extend to multiple feature maps.
  • I assume the size (C, H, W) of features between teacher and student are the same. If not, you could employ 1*1 conv, linear or pooling to rectify them.

Results

• The trained baseline models are used as teachers. For fair comparison, all the student nets have same initialization with the baseline models.
• The initial models, trained models and training logs are uploaded here (code: ezed).
• The trade-off parameter --lambda_kd and other hyper-parameters are not chosen carefully. Thus the following results do not reflect which method is better than the others.
• Some relation based methods, e.g. PKT, RKD and CC, have less effectiveness on CIFAR100 dataset. It may be because there are more inter classes but less intra classes in one batch. You could increase the batch size, create memory bank or design advance batch sampling methods.

Teacher	Student	Name	CIFAR10	CIFAR100
-	resnet-20	Baseline	92.37%	68.92%
resnet-20	resnet-20	Logits	93.30%	70.36%
resnet-20	resnet-20	ST	93.12%	70.27%
resnet-20	resnet-20	AT	92.89%	69.70%
resnet-20	resnet-20	Fitnet	92.73%	70.08%
resnet-20	resnet-20	NST	92.79%	69.21%
resnet-20	resnet-20	PKT	92.50%	69.25%
resnet-20	resnet-20	FSP	92.76%	69.61%
resnet-20	resnet-20	FT	92.98%	69.90%
resnet-20	resnet-20	RKD	92.72%	69.48%
resnet-20	resnet-20	AB	93.04%	69.96%
resnet-20	resnet-20	SP	92.88%	69.85%
resnet-20	resnet-20	Sobolev	92.78%	69.39%
resnet-20	resnet-20	BSS	92.58%	69.96%
resnet-20	resnet-20	CC	93.01%	69.27%
resnet-20	resnet-20	LwM	92.80%	69.23%
resnet-20	resnet-20	IRG	92.77%	70.37%
resnet-20	resnet-20	VID	92.61%	69.39%
resnet-20	resnet-20	OFD	92.82%	69.93%
resnet-20	resnet-20	AFD	92.56%	69.63%
resnet-20	resnet-20	CRD	92.96%	70.33%

Teacher	Student	Name	CIFAR10	CIFAR100
-	resnet-20	Baseline	92.37%	68.92%
-	resnet-110	Baseline	93.86%	73.15%
resnet-110	resnet-20	Logits	92.98%	69.78%
resnet-110	resnet-20	ST	92.82%	70.06%
resnet-110	resnet-20	AT	93.21%	69.28%
resnet-110	resnet-20	Fitnet	93.04%	69.81%
resnet-110	resnet-20	NST	92.83%	69.31%
resnet-110	resnet-20	PKT	93.01%	69.31%
resnet-110	resnet-20	FSP	92.78%	69.78%
resnet-110	resnet-20	FT	93.01%	69.49%
resnet-110	resnet-20	RKD	93.21%	69.36%
resnet-110	resnet-20	AB	92.96%	69.41%
resnet-110	resnet-20	SP	93.30%	69.45%
resnet-110	resnet-20	Sobolev	92.60%	69.23%
resnet-110	resnet-20	BSS	92.78%	69.71%
resnet-110	resnet-20	CC	92.98%	69.33%
resnet-110	resnet-20	LwM	92.52%	69.11%
resnet-110	resnet-20	IRG	93.13%	69.36%
resnet-110	resnet-20	VID	92.98%	69.49%
resnet-110	resnet-20	OFD	93.13%	69.81%
resnet-110	resnet-20	AFD	92.92%	69.60%
resnet-110	resnet-20	CRD	92.92%	70.80%

Teacher	Student	Name	CIFAR10	CIFAR100
-	resnet-110	Baseline	93.86%	73.15%
resnet-110	resnet-110	Logits	94.38%	74.89%
resnet-110	resnet-110	ST	94.59%	74.33%
resnet-110	resnet-110	AT	94.42%	74.64%
resnet-110	resnet-110	Fitnet	94.43%	73.63%
resnet-110	resnet-110	NST	94.43%	73.55%
resnet-110	resnet-110	PKT	94.35%	73.74%
resnet-110	resnet-110	FSP	94.39%	73.59%
resnet-110	resnet-110	FT	94.30%	74.72%
resnet-110	resnet-110	RKD	94.39%	73.78%
resnet-110	resnet-110	AB	94.63%	73.91%
resnet-110	resnet-110	SP	94.45%	74.07%
resnet-110	resnet-110	Sobolev	94.26%	73.14%
resnet-110	resnet-110	BSS	94.19%	73.87%
resnet-110	resnet-110	CC	94.49%	74.43%
resnet-110	resnet-110	LwM	94.19%	73.28%
resnet-110	resnet-110	IRG	94.44%	74.96%
resnet-110	resnet-110	VID	94.25%	73.63%
resnet-110	resnet-110	OFD	94.38%	74.11%
resnet-110	resnet-110	AFD	94.44%	73.90%
resnet-110	resnet-110	CRD	94.30%	75.44%

Net1	Net2	Name	CIFAR10	CIFAR100
-	resnet-20	baseline	92.37%	68.92%
-	resnet-110	baseline	93.86%	73.15%
resnet20	resnet20	DML	93.07%/93.37%	70.39%/70.22%
resnet110	resnet20	DML	94.45%/92.92%	74.53%/70.29%
resnet110	resnet110	DML	94.74%/94.79%	74.72%/75.55%

Todo List

• KDSVD (now has some bugs)
• QuEST: Quantized Embedding Space for Transferring Knowledge
• EEL: Learning an Evolutionary Embedding via Massive Knowledge Distillation
• OnAdvFD: Feature-map-level Online Adversarial Knowledge Distillation
• CS-KD: Regularizing Class-wise Predictions via Self-knowledge Distillation
• PAD: Prime-Aware Adaptive Distillation
• DCM: Knowledge Transfer via Dense Cross-Layer Mutual-Distillation
• ESKD: On the Efficacy of Knowledge Distillation
• GKA: Teachers Do More Than Teach: Compressing Image-to-Image Models
• KD-fn: Feature Normalized Knowledge Distillation for Image Classification
• FSD: Knowledge Distillation via Adaptive Instance Normalization
• CSKD: Improving Knowledge Distillation via Category Structure
• CD & GKD: Channel Distillation: Channel-Wise Attention for Knowledge Distillation
• CRCD: Complementary Relation Contrastive Distillation
• MGD: Matching Guided Distillation
• SSKD: Knowledge Distillation Meets Self-Supervision
• KR: Distilling Knowledge via Knowledge Review
• AFD-SAD: Show, Attend and Distill: Knowledge Distillation via Attention-based Feature Matching
• SRRL: Knowledge Distillation via Softmax Regression Representation Learning
• SemCKD: Cross-Layer Distillation with Semantic Calibration
• SKD: Reducing the Teacher-Student Gap via Spherical Knowledge Disitllation
• IFDM: Heterogeneous Knowledge Distillation using Information Flow Modeling
• LKD: Local Correlation Consistency for Knowledge Distillation
• HKD: Distilling Holistic Knowledge with Graph Neural Networks
• LONDON: Lipschitz Continuity Guided Knowledge Distillation
• CDD: Channel-Wise Knowledge Distillation for Dense Prediction
• SCKD: Student Customized Knowledge Distillation: Bridging the Gap Between Student and Teacher
• ICKD: Exploring Inter-Channel Correlation for Diversity-preserved Knowledge Distillation
• DKMF: Distilling Knowledge by Mimicking Features
• CRCD: Complementary Relation Contrastive Distillation
• WCoRD: Wasserstein Contrastive Representation Distillation
• LKD: Local Correlation Consistency for Knowledge Distillation
• KDCL: Online Knowledge Distillation via Collaborative Learning
• ONE: Knowledge Distillation by On-the-Fly Native Ensemble

Requirements

• python 3.7
• pytorch 1.3.1
• torchvision 0.4.2

Acknowledgements

This repo is partly based on the following repos, thank the authors a lot.

• HobbitLong/RepDistiller
• bhheo/BSS_distillation
• clovaai/overhaul-distillation
• passalis/probabilistic_kt
• lenscloth/RKD

If you employ the listed KD methods in your research, please cite the corresponding papers.