This article introduces a novel 2D shape data augmentation approach based on intra-class
shape space exploration. The proposed method relies on a geodesic interpolation between …

As Deep Neural Networks have achieved thrilling breakthroughs in the past decade, data
augmentations have garnered increasing attention as regularization techniques when …

Most existing keyword spotting research focuses on conditions with slight or moderate noise.
In this paper, we try to tackle a more challenging task: detecting keywords buried under …

Few-shot keyword spotting (KWS) aims to detect unknown keywords with limited training
samples. A commonly used approach is the pre-training and fine-tuning framework. While …

Data augmentation techniques play an important role in enhancing the performance of deep
learning models. Despite their proven benefits in computer vision tasks, their application in …

Learning in the reproducing kernel Hilbert space (RKHS) such as the support vector
machine has been recognized as a promising technique. It continues to be highly effective …

In recent decades, many researchers have directed their focus towards confidence
calibration of deep neural networks because trustworthiness is equally important as …

Regularizing neural networks is important for anticipating model behavior in regions of the
data space that are not well represented. In this work, we propose a regularization technique …

Mixup is a data augmentation technique that gener-ates new samples using a convex
combination of samples. Despite its remarkable simplicity, this method significantly improves …

Out-of-distribution (OOD) detection methods, especially post-hoc methods, rely on off-the-
shelf pre-trained models. Existing literature shows how OOD and ID performance are …