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.