Due to the proliferation of biomedical imaging modalities, such as Photoacoustic
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …

Convolutional neural networks (CNNs) have been applied to visual tasks since the late
1980s. However, despite a few scattered applications, they were dormant until the mid …

The generalization and learning speed of a multi-class neural network can often be
significantly improved by using soft targets that are a weighted average of the hard targets …

Semi-supervised learning, ie jointly learning from labeled and unlabeled samples, is an
active research topic due to its key role on relaxing human supervision. In the context of …

Label smoothing is an effective regularization tool for deep neural networks (DNNs), which
generates soft labels by applying a weighted average between the uniform distribution and …

Label smoothing is commonly used in training deep learning models, wherein one-hot
training labels are mixed with uniform label vectors. Empirically, smoothing has been shown …

Image classification systems recently made a giant leap with the advancement of deep
neural networks. However, these systems require an excessive amount of labeled data to be …

Automatic modulation classification is an essential and challenging topic in the development
of cognitive radios, and it is the cornerstone of adaptive modulation and demodulation …

In this paper, we introduce Random Erasing, a new data augmentation method for training
the convolutional neural network (CNN). In training, Random Erasing randomly selects a …

Automatic arrhythmia detection from Electrocardiogram (ECG) plays an important role in
early prevention and diagnosis of cardiovascular diseases. Convolutional neural network …