Open-set Recognition (OSR) aims to identify test samples whose classes are not seen
during the training process. Recently, Unified Open-set Recognition (UOSR) has been …

When infrared small-dim target images under strong background clutters are employed to
train a deep learning (DL)-based detection network, the model becomes biased toward the …

Communication-efficient federated learning benefits from neural network pruning, as it
speeds up training and reduces model size. However, existing pruning techniques may not …

Unsupervised out-of-distribution (U-OOD) detection is to identify OOD data samples with a
detector trained solely on unlabeled in-distribution (ID) data. The likelihood function …

Anomaly detection in time-series data is crucial for identifying faults, failures, threats, and
outliers across a range of applications. Recently, deep learning techniques have been …

We present a self-supervised variational autoencoder (VAE) to jointly learn disentangled
and dependent hidden factors and then enhance disentangled representation learning by a …

To make an employee roster, photo album, or training dataset of generative models, one
needs to collect high-quality images while dismissing low-quality ones. This study …

Abstract Face Forgery Detection (FFD) plays a pivotal role in preserving privacy and
bolstering information security by identifying counterfeit face images sourced from the …

Current deep learning models are trained to fit the training set distribution. Despite the
remarkable advancements attributable to cutting-edge architectural designs, these models …

Autoencoders are used in a variety of safety-critical applications. Uncertainty quantification
is a key component to bolster the trustworthiness of such models. With the growing …