Can-we-Gain-More-from-Orthogonality

Code Implementation for Restricted Isometry Property(RIP) based Orthogonal Regularizers, proposed for Image Classification Task, for various State-of-art ResNet based architectures.

This repositry provides an introduction, implementation and result achieved in the paper: "Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?", NIPS 2018 [pdf]

Introduction

Orthogonal Network Weights are found to be a favorable property for training deep convolutional neural networks.Through this work, we look to find alternate and more effective ways to enforce orthogonality to deep CNNs. We develop novel orthogonality regularizations on training deep CNNs, utilizing various advanced analytical tools such as mutual coherence and restricted isometry property. These plug-and-play regularizations can be conveniently incorporated into training almost any CNN without extra hassle. We then benchmark their effects on state-of-the-art models: ResNet, WideResNet, and ResNeXt, on several most popular computer vision datasets: CIFAR-10, CIFAR-100, SVHN and ImageNet. We observe consistent performance gains after applying those proposed regularizations, in terms of both the final accuracies achieved, and faster and more stable convergences.

Enviroment and Datasets Used

• Linux
• Pytorch 4.0
• Keras 2.2.4
• CUDA 9.1
• Cifar10 and Cifar100
• SVHN
• ImageNet

Architecture Used

Wide Resnet.pytorch

Usage Wide-Resnet CIFAR

To train on Cifar-10 using 2 gpu:

CUDA_VISIBLE_DEVICES=6,7 python train_n.py --ngpu 2

To train on Cifar-100 using 2 gpu:

CUDA_VISIBLE_DEVICES=6,7 python train_n.py --ngpu 2 --dataset cifar100

After train phase, you can check saved model in the runs folder.

Usage Wide-Resnet SVHN

CUDA_VISIBEL_DEVICES=0 python train.py --dataset svhn --model wideresnet --learning_rate 0.01 --epochs 160

Network	CIFAR-10	CIFAR-100	SVHN
WideResNet	4.16	20.50	1.60
WideResNet + Reg	3.60	18.19	1.52

Other frameworks

• torch (@facebookresearch). (Original) Cifar and Imagenet

Acknowledgement

• wideresnet-pytorch
• densenet-pytorch
• Wide Residual Networks (BMVC 2016) http://arxiv.org/abs/1605.07146 by Sergey Zagoruyko and Nikos Komodakis.
• cutout-svhn

Cite

@article{xie2016aggregated,
  title={Aggregated residual transformations for deep neural networks},
  author={Xie, Saining and Girshick, Ross and Doll{\'a}r, Piotr and Tu, Zhuowen and He, Kaiming},
  journal={arXiv preprint arXiv:1611.05431},
  year={2016}
}

@article{DBLP:journals/corr/ZagoruykoK16,
  author    = {Sergey Zagoruyko and
               Nikos Komodakis},
  title     = {Wide Residual Networks},
  journal   = {CoRR},
  volume    = {abs/1605.07146},
  year      = {2016},
  url       = {http://arxiv.org/abs/1605.07146},
  archivePrefix = {arXiv},
  eprint    = {1605.07146},
  timestamp = {Mon, 13 Aug 2018 16:46:42 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/ZagoruykoK16},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/HeZRS15,
  author    = {Kaiming He and
               Xiangyu Zhang and
               Shaoqing Ren and
               Jian Sun},
  title     = {Deep Residual Learning for Image Recognition},
  journal   = {CoRR},
  volume    = {abs/1512.03385},
  year      = {2015},
  url       = {http://arxiv.org/abs/1512.03385},
  archivePrefix = {arXiv},
  eprint    = {1512.03385},
  timestamp = {Mon, 13 Aug 2018 16:46:56 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/HeZRS15},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}