In recent years, deep artificial neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical survey …

Objective measures of image quality generally operate by comparing pixels of a “degraded”
image to those of the original. Relative to human observers, these measures are overly …

The heavy reliance on data is one of the major reasons that currently limit the development
of deep learning. Data quality directly dominates the effect of deep learning models, and the …

Semi-supervised learning lately has shown much promise in improving deep learning
models when labeled data is scarce. Common among recent approaches is the use of …

LiDAR point clouds, which are usually scanned by rotating LiDAR sensors continuously,
capture precise geometry of the surrounding environment and are crucial to many …

We present EDA: easy data augmentation techniques for boosting performance on text
classification tasks. EDA consists of four simple but powerful operations: synonym …

Large deep neural networks are powerful, but exhibit undesirable behaviors such as
memorization and sensitivity to adversarial examples. In this work, we propose mixup, a …

We propose a novel data augmentation for labeled sentences called contextual
augmentation. We assume an invariance that sentences are natural even if the words in the …

For classification, support vector machines (SVMs) have recently been introduced and
quickly became the state of the art. Now, the incorporation of prior knowledge into SVMs is …

Data augmentation methods are often applied to prevent overfitting and improve
generalization of deep neural network models. Recently proposed contextual augmentation …