Real-world datasets are often highly class-imbalanced, which can adversely impact the
performance of deep learning models. The majority of research on training neural networks …

The tasks of few-shot, one-shot, and zero-shot learning—or collectively “low-shot
learning”(LSL)—at first glance are quite similar to the long-standing task of class imbalanced …

Few-shot learning (FSL) algorithms are commonly trained through meta-learning (ML),
which exposes models to batches of tasks sampled from a meta-dataset to mimic tasks seen …

This paper proposes a method to treat the classification of imbalanced data by adding noise
to the feature space of convolutional neural network (CNN) without changing a data set …

The modern tasks of few-shot, one-shot, and zero-shot learning-or collectively Low-Shot
Learning (LSL)-at first glance are quite similar to the long-standing task of class-imbalanced …

Surrogate models are effective tools for accelerated design of complex systems. The result
of a design optimization procedure using surrogate models can be used to initialize an …

Unlike carefully curated academic benchmarks, real-world datasets are often highly class-
imbalanced, involving training and test sets which contain few examples from certain …

Global warming has posed non-negligible effects on regional extreme precipitation changes
and increased the uncertainties when meteorologists predict such extremes. More …

The unequal class distributions result in two types of prediction errors that incur different
costs in imbalanced credit data sets. These are monetary losses for the misclassified …

Class imbalance is one of the main weaknesses in modern machine learning methods. In
this area, datasets with an imbalance ratio greater than 1: 100 are defined as severely …