Regularization method

Regional Patch-Based FeatureInterpolation Method for EffectiveRegularization

• Paper link This paper has been published in IEEE access.
• Deep Convolutional Neural Networks (CNNs) can be overly dependent on training data, causing a generalization problem in which trained models may not predict real-world datasets.
• Propose a regularization method that applies both image manipulation and feature map regularization based on patches.
• In the image manipulation stage, two images can be combined, based on the patch to generate a new image without information loss.
• we added patch-based other image features through interpolation between feature maps of new images in the feature map regularization stage.
• This allows the model to simultaneously learn the image distribution of other labels and to generate a model robust to noise

Getting Started

Requirements

• python3.6
• PyTorch
• Dataset (CIFAR10, 100 & TinyImageNet)

Train

python train.py 

Test

python test.py