Adaout is a practical and flexible regularization method with high generalization and interpretability

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Deep LearningAdaout
Overview

Adaout

Adaout is a practical and flexible regularization method with high generalization and interpretability.

Requirements

  • python 3.6 (Anaconda version >=5.2.0 is recommended)
  • torch (torch version >=1.1.0 is recommended)
  • torchvision (torchvision version >=0.3.0 is recommended)
  • pandas
  • numpy
  • NVIDIA GPU + CUDA CuDNN

Datasets

  • CIFAR-10, CIFAR-100, SVHN, ImageNet and others

Getting started

  • Download datasets and extract it inside data
  • Train: python train.py, python train100.py or python train_svhn.py
  • Evaluate:
    • Pretrained models for CIFAR-10 and CIFAR-100 are available at this link. Download and extract them in the save_model/resnet56_10 or save_model/resnet56_100 directory.
    • You should achieve about 94.63% accuracy on CIFAR-10, and 74.18% accuracy on CIFAR-100 datasets.
Owner
lambett
lambett
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