Hashing, which encodes raw data into compact binary codes, has grown in popularity for
large-scale image retrieval due to its storage and computation efficiency. Although deep …

Deep learning with noisy labels is challenging and inevitable in many circumstances.
Existing methods reduce the impact of noise samples by reducing loss weights of uncertain …

Abstract Facial Expression Recognition (FER) suffers from data uncertainties caused by
ambiguous facial images and annotators' subjectiveness, resulting in excursive semantic …

Existing deep learning methods for fine-grained visual recognition often rely on large-scale,
well-annotated training data. Obtaining fine-grained annotations in the wild typically requires …

Deep learning with noisy labels is a challenging task. Recent prominent methods that build
on a specific sample selection (SS) strategy and a specific semi-supervised learning (SSL) …

The design of highly accurate deep learning architectures is related to different parameters
through different optimizations at different levels of the design. While the architectural design …

Meta learning has attracted much attention recently in machine learning community.
Contrary to conventional machine learning aiming to learn inherent prediction rules to …

Deep neural network models are especially susceptible to noise in annotated labels. In the
real world, annotated data typically contains noise caused by a variety of factors such as …

Abstract Background The Rey Complex Figure Test (RCFT) has been widely used to
evaluate the neurocognitive functions in various clinical groups with a broad range of ages …

Machine learning (ML) has been adopted in many safety-critical applications like automated
driving and medical diagnosis. Incorrect decisions by ML models can lead to catastrophic …