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Implicit Semantic Data Augmentation for Deep Networks (NeurIPS 2019)

Code for the paper Implicit Semantic Data Augmentation for Deep Networks.

Update on 2020/04/25: Release Pre-trained Models on ImageNet.

Update on 2020/04/24: Release Code for Image Classification on ImageNet and Semantic Segmentation on Cityscapes.

Introduction

In this paper, we propose a novel implicit semantic data augmentation (ISDA) approach to complement traditional augmentation techniques like flipping, translation or rotation. ISDA consistently improves the generalization performance of popular deep networks on supervised & semi-supervised image classification, semantic segmentation, object detection and instance segmentation.

Citation

If you find this work useful or use our code in your own research, please use the following bibtex:

@inproceedings{NIPS2019_9426,
        title = {Implicit Semantic Data Augmentation for Deep Networks},
       author = {Wang, Yulin and Pan, Xuran and Song, Shiji and Zhang, Hong and Huang, Gao and Wu, Cheng},
    booktitle = {Advances in Neural Information Processing Systems 32},
        pages = {12635--12644},
         year = {2019},
}

Get Started

Please go to the folder Image classification on CIFAR, Image classification on ImageNet and Semantic segmentation on Cityscapes for specific docs.

Pre-trained Models on ImageNet

  • Measured by Top-1 error.
Model Params Baseline ISDA Model
ResNet-50 25.6M 23.0 21.9 Tsinghua Cloud / Google Drive
ResNet-101 44.6M 21.7 20.8 Tsinghua Cloud / Google Drive
ResNet-152 60.3M 21.3 20.3 Tsinghua Cloud / Google Drive
DenseNet-BC-121 8.0M 23.7 23.2 Tsinghua Cloud / Google Drive
DenseNet-BC-265 33.3M 21.9 21.2 Tsinghua Cloud / Google Drive
ResNeXt50, 32x4d 25.0M 22.5 21.3 Tsinghua Cloud / Google Drive
ResNeXt101, 32x8d 88.8M 21.1 20.1 Tsinghua Cloud / Google Drive

Results

  • Supervised image classification on ImageNet

  • Complementing traditional data augmentation techniques

  • Semi-supervised image classification on CIFAR & SVHN

  • Semantic segmentation on Cityscapes

  • Object detection on MS COCO

  • Instance segmentation on MS COCO

Acknowledgment

Our code for semantic segmentation is mainly based on pytorch-segmentation-toolbox.

To Do

Update code for semi-supervised learning.