Contrastive Learning is self-supervised representation learning by training a model to
differentiate between similar and dissimilar samples. It has been shown to be effective and …

Self-supervised contrastive learning has become a key technique in deep learning,
particularly in time series analysis, due to its ability to learn meaningful representations …

One-class classification (OCC) involves predicting whether a new data is normal or
anomalous based solely on the data from a single class during training. Various attempts …

Deep learning models, which require abundant labeled data for training, are expensive and
time-consuming to implement, particularly in medical imaging. Active learning (AL) aims to …

Although there has been much progress in the area of facial expression recognition (FER),
most existing methods suffer when presented with images that have been captured from …

In Siamese-network-based self-supervised learning (SSL), multilevel supervision (MLS) is a
natural extension to enforce intermediate representations' consistency against data …

Despite significant progress in Anomaly Detection (AD), the robustness of existing detection
methods against adversarial attacks remains a challenge, compromising their reliability in …

Cyberbullying on social media platforms is pervasive and challenging to detect due to
linguistic subtleties and the need for extensive data annotation. We introduce a Deep …

In ambient assisted living, human activity recognition from time series sensor data mainly
focuses on predefined activities, often overlooking new activity patterns. We propose CLAN …

In recent years, self-supervised contrastive learning has emerged as a distinguished
paradigm in the artificial intelligence landscape. It facilitates unsupervised feature learning …