Image classification with small datasets has been an active research area in the recent past.
However, as research in this scope is still in its infancy, two key ingredients are missing for …

Solving image classification tasks given small training datasets remains an open challenge
for modern computer vision. Aggressive data augmentation and generative models are …

Numerical experiments demonstrate that deep neural network classifiers progressively
separate class distributions around their mean, achieving linear separability on the training …

Convolutional neural networks (CNNs) learn filters in order to capture local correlation
patterns in feature space. We propose to learn these filters as combinations of preset …

Data-efficient image classification using deep neural networks in settings, where only small
amounts of labeled data are available, has been an active research area in the recent past …

Abstract Machine learning algorithms are widely employed across various applications and
fields. Novel technologies in automated machine learning ease the complexity of algorithm …

Convolutional neural networks have demonstrated impressive results in many computer
vision tasks. However, the increasing size of these networks raises concerns about the …

This paper focuses on improving the mathematical interpretability of convolutional neural
networks (CNNs) in the context of image classification. Specifically, we tackle the instability …

Spectral representations have been introduced into deep convolutional neural networks
(CNNs) mainly for accelerating convolutions and mitigating information loss. However …

Mixup is a widely adopted strategy for training deep networks, where additional samples are
augmented by interpolating inputs and labels of training pairs. Mixup has shown to improve …