Deep neural networks represent a compelling technique to tackle complex real-world
problems, but are over-parameterized and often suffer from over-or under-confident …

Deep learning is a machine learning technique that has revolutionized the research
community due to its impressive results on various real-life problems. Recently, ensembles …

Training deep learning models with unequal sample weights has been shown to enhance
model performance in various typical learning scenarios, particularly for imbalanced and …

Treating each training sample unequally is prevalent in many machine-learning tasks.
Numerous weighting schemes have been proposed. Some schemes take the easy-first …

Sample weighting is widely used in deep learning. A large number of weighting methods
essentially utilize the learning difficulty of training samples to calculate their weights. In this …

Food recognition has recently attracted attention of many researchers. However, high food
ambiguity, inter-class variability and intra-class similarity define a real challenge for the …

''Easy/hard sample” is a popular parlance in machine learning. Learning difficulty of samples
refers to how easy/hard a sample is during a learning procedure. An increasing need of …

Food image classification and recognition is an emerging research area due to its growing
importance in the medical and health industries. As India is growing digitally rapidly, an …

[eng] Several computer vision approaches have been proposed for tackling food analysis
problems, due to the challenging problem it poses, the ease collection of food images, and …