Using mixup as regularization and tuning hyper-parameters for ResNets

11/23/2021

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by   Venkata Bhanu Teja Pallakonda, et al.

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While novel computer vision architectures are gaining traction, the impact of model architectures is often related to changes or exploring in training methods. Identity mapping-based architectures ResNets and DenseNets have promised path-breaking results in the image classification task and are go-to methods for even now if the data given is fairly limited. Considering the ease of training with limited resources this work revisits the ResNets and improves the ResNet50 <cit.> by using mixup data-augmentation as regularization and tuning the hyper-parameters.