[NeurIPS-2021] Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data

Overview

MosaicKD

Code for NeurIPS-21 paper "Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data"

1. Motivation

Natural images share common local patterns. In MosaicKD, these local patterns are first dissembled from OOD data and then assembled to synthesize in-domain data, making OOD-KD feasible.

2. Method

MosaicKD establishes a four-player minimax game between a generator G, a patch discriminator D, a teacher model T and a student model S. The generator, as those in prior GANs, takes as input a random noise vector and learns to mosaic synthetic in-domain samples with locally-authentic and globally-legitimate distributions, under the supervisions back-propagated from the other three players.

3. Reproducing our results

3.1 Prepare teachers

Please download our pre-trained models from Dropbox (266 M) and extract them as "checkpoints/pretrained/*.pth". You can also train your own models as follows:

python train_scratch.py --lr 0.1 --batch-size 256 --model wrn40_2 --dataset cifar100

3.2 OOD-KD: CIFAR-100 (ID) + CIFAR10 (OOD)

  • Vanilla KD (Blind KD)

    python kd_vanilla.py --lr 0.1 --batch-size 128 --teacher wrn40_2 --student wrn16_1 --dataset cifar100 --unlabeled cifar10 --epoch 200 --gpu 0 
  • Data-Free KD (DFQAD)

    python kd_datafree.py --lr 0.1 --batch-size 256 --teacher wrn40_2 --student wrn16_1 --dataset cifar100 --unlabeled cifar10 --epoch 200 --lr 0.1 --local 1 --align 1 --adv 1 --balance 10 --gpu 0
  • MosaicKD (This work)

    python kd_mosaic.py --lr 0.1 --batch-size 256 --teacher wrn40_2 --student wrn16_1 --dataset cifar100 --unlabeled cifar10 --epoch 200 --lr 0.1 --local 1 --align 1 --adv 1 --balance 10 --gpu 0

3.3 OOD-KD: CIFAR-100 (ID) + ImageNet/Places365 OOD Subset (OOD)

  • Prepare 32x32 datasets
    Please prepare the 32x32 ImageNet following the instructions from https://patrykchrabaszcz.github.io/Imagenet32/ and extract them as "data/ImageNet_32x32/train" and "data/ImageNet_32x32/val". You can prepare Places365 in the same way.

  • MosaicKD on OOD subset
    As ImageNet & Places365 contain a large number of in-domain samples, we construct OOD subset for training. Please run the scripts with ''--ood_subset'' to enable subset selection.

    python kd_mosaic.py --lr 0.1 --batch-size 256 --teacher wrn40_2 --student wrn16_1 --dataset cifar100 --unlabeled cifar10 --epoch 200 --lr 0.1 --local 1 --align 1 --adv 1 --balance 10 --ood_subset --gpu 0

4. Visualization of synthetic data

5. Citation

If you found this work useful for your research, please cite our paper:

@article{fang2021mosaicking,
  title={Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data},
  author={Gongfan Fang and Yifan Bao and Jie Song and Xinchao Wang and Donglin Xie and Chengchao Shen and Mingli Song},
  journal={arXiv preprint arXiv:2110.15094},
  year={2021}
}
You might also like...
TorchDistiller - a collection of the open source pytorch code for knowledge distillation, especially for the perception tasks, including semantic segmentation, depth estimation, object detection and instance segmentation.

This project is a collection of the open source pytorch code for knowledge distillation, especially for the perception tasks, including semantic segmentation, depth estimation, object detection and instance segmentation.

PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation
PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation

PocketNet This is the official repository of the paper: PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and M

This is the official pytorch implementation of Student Helping Teacher: Teacher Evolution via Self-Knowledge Distillation(TESKD)
This is the official pytorch implementation of Student Helping Teacher: Teacher Evolution via Self-Knowledge Distillation(TESKD)

Student Helping Teacher: Teacher Evolution via Self-Knowledge Distillation (TESKD) By Zheng Li[1,4], Xiang Li[2], Lingfeng Yang[2,4], Jian Yang[2], Zh

Instance-conditional Knowledge Distillation for Object Detection

Instance-conditional Knowledge Distillation for Object Detection This is a MegEngine implementation of the paper "Instance-conditional Knowledge Disti

Knowledge Distillation Toolbox for Semantic Segmentation
Knowledge Distillation Toolbox for Semantic Segmentation

SegDistill: Toolbox for Knowledge Distillation on Semantic Segmentation Networks This repo contains the supported code and configuration files for Seg

Focal and Global Knowledge Distillation for Detectors

FGD Paper: Focal and Global Knowledge Distillation for Detectors Install MMDetection and MS COCO2017 Our codes are based on MMDetection. Please follow

Pytorch implementation for Patient Knowledge Distillation for BERT Model Compression

Patient Knowledge Distillation for BERT Model Compression Knowledge distillation for BERT model Installation Run command below to install the environm

PyTorch implementation of paper A Fast Knowledge Distillation Framework for Visual Recognition.
PyTorch implementation of paper A Fast Knowledge Distillation Framework for Visual Recognition.

FKD: A Fast Knowledge Distillation Framework for Visual Recognition Official PyTorch implementation of paper A Fast Knowledge Distillation Framework f

Official implementation of the paper
Official implementation of the paper "Lightweight Deep CNN for Natural Image Matting via Similarity Preserving Knowledge Distillation"

Lightweight-Deep-CNN-for-Natural-Image-Matting-via-Similarity-Preserving-Knowledge-Distillation Introduction Accepted at IEEE Signal Processing Letter

Comments
  • CVE-2007-4559 Patch

    CVE-2007-4559 Patch

    Patching CVE-2007-4559

    Hi, we are security researchers from the Advanced Research Center at Trellix. We have began a campaign to patch a widespread bug named CVE-2007-4559. CVE-2007-4559 is a 15 year old bug in the Python tarfile package. By using extract() or extractall() on a tarfile object without sanitizing input, a maliciously crafted .tar file could perform a directory path traversal attack. We found at least one unsantized extractall() in your codebase and are providing a patch for you via pull request. The patch essentially checks to see if all tarfile members will be extracted safely and throws an exception otherwise. We encourage you to use this patch or your own solution to secure against CVE-2007-4559. Further technical information about the vulnerability can be found in this blog.

    If you have further questions you may contact us through this projects lead researcher Kasimir Schulz.

    opened by TrellixVulnTeam 0
Owner
ZJU-VIPA
Laboratory of Visual Intelligence and Pattern Analysis
ZJU-VIPA
[IJCAI-2021] A benchmark of data-free knowledge distillation from paper "Contrastive Model Inversion for Data-Free Knowledge Distillation"

DataFree A benchmark of data-free knowledge distillation from paper "Contrastive Model Inversion for Data-Free Knowledge Distillation" Authors: Gongfa

ZJU-VIPA 47 Jan 9, 2023
TF2 implementation of knowledge distillation using the "function matching" hypothesis from the paper Knowledge distillation: A good teacher is patient and consistent by Beyer et al.

FunMatch-Distillation TF2 implementation of knowledge distillation using the "function matching" hypothesis from the paper Knowledge distillation: A g

Sayak Paul 67 Dec 20, 2022
Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting (ICCV, 2021)

DKPNet ICCV 2021 Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting Baseline of DKPNet is availa

null 19 Oct 14, 2022
Official implementation for (Refine Myself by Teaching Myself : Feature Refinement via Self-Knowledge Distillation, CVPR-2021)

FRSKD Official implementation for Refine Myself by Teaching Myself : Feature Refinement via Self-Knowledge Distillation (CVPR-2021) Requirements Pytho

null 75 Dec 28, 2022
The official implementation of CVPR 2021 Paper: Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation.

Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation This repository is the official implementation of CVPR 2021 paper:

null 9 Nov 14, 2022
Code implementation of Data Efficient Stagewise Knowledge Distillation paper.

Data Efficient Stagewise Knowledge Distillation Table of Contents Data Efficient Stagewise Knowledge Distillation Table of Contents Requirements Image

IvLabs 112 Dec 2, 2022
Light-weight network, depth estimation, knowledge distillation, real-time depth estimation, auxiliary data.

light-weight-depth-estimation Boosting Light-Weight Depth Estimation Via Knowledge Distillation, https://arxiv.org/abs/2105.06143 Junjie Hu, Chenyou F

Junjie Hu 13 Dec 10, 2022
Code for CVPR2021 "Visualizing Adapted Knowledge in Domain Transfer". Visualization for domain adaptation. #explainable-ai

Visualizing Adapted Knowledge in Domain Transfer @inproceedings{hou2021visualizing, title={Visualizing Adapted Knowledge in Domain Transfer}, auth

Yunzhong Hou 80 Dec 25, 2022
Block-wisely Supervised Neural Architecture Search with Knowledge Distillation (CVPR 2020)

DNA This repository provides the code of our paper: Blockwisely Supervised Neural Architecture Search with Knowledge Distillation. Illustration of DNA

Changlin Li 215 Dec 19, 2022
AMTML-KD: Adaptive Multi-teacher Multi-level Knowledge Distillation

AMTML-KD: Adaptive Multi-teacher Multi-level Knowledge Distillation

Frank Liu 26 Oct 13, 2022