The Implicit Semantic Data Augmentation (ISDA) algorithm implemented in Pytorch.
(NeurIPS 2019) Implicit Semantic Data Augmentation for Deep Networks
(T-PAMI) Regularizing Deep Networks with Semantic Data Augmentation
Update on 2021/04/23: Release Code for Visualizing Deep Features on ImageNet!
Update on 2021/01/17: Journal Version of ISDA is Accepted by T-PAMI!
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.
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.
If you find this work valuable or use our code in your own research, please consider citing us with 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 (NeurIPS)},
pages = {12635--12644},
year = {2019},
}
@article{wang2021regularizing,
title = {Regularizing deep networks with semantic data augmentation},
author = {Wang, Yulin and Huang, Gao and Song, Shiji and Pan, Xuran and Xia, Yitong and Wu, Cheng},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
year = {2021},
doi = {10.1109/TPAMI.2021.3052951}
}
Please go to the folder Image classification on CIFAR, Image classification on ImageNet, Semantic segmentation on Cityscapes and Visualizing deep features for specific docs.
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 |
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
Our code for semantic segmentation is mainly based on pytorch-segmentation-toolbox.
Update code for semi-supervised learning.