The purpose of this study is to examine existing deep learning techniques for addressing
class imbalanced data. Effective classification with imbalanced data is an important area of …

Any computer vision application development starts off by acquiring images and data, then
preprocessing and pattern recognition steps to perform a task. When the acquired images …

Prediction-powered inference is a framework for performing valid statistical inference when
an experimental dataset is supplemented with predictions from a machine-learning system …

Deep learning has recently unleashed the ability for Machine learning (ML) to make
unparalleled strides. It did so by confronting and successfully addressing, at least to a …

The existence of class imbalance in a dataset can greatly bias the classifier towards majority
classification. This discrepancy can pose a serious problem for deep learning models, which …

In recent years, there has been an enormous interest in using deep learning to classify
underwater images to identify various objects, such as fishes, plankton, coral reefs …

Plankton image classification plays an important role in the ocean ecosystems research.
Recently, a large scale database for plankton classification with over 3 million images …

Recent studies have shown that features from deep convolutional neural networks learned
using large labeled datasets, like ImageNet, provide effective representations for a variety of …

Advances in both hardware and software are enabling rapid proliferation of in situ plankton
imaging methods, requiring more effective machine learning approaches to image …

Studying marine plankton is critical to assessing the health of the world's oceans. To sample
these important populations, oceanographers are increasingly using specially engineered in …