Deep-learning techniques have achieved great success in remote-sensing image change
detection. Most of them are supervised techniques, which usually require large amounts of …

Predictive uncertainty estimation is essential for deploying Deep Neural Networks in real-
world autonomous systems. However, most successful approaches are computationally …

The acquisition of soil information using infrared spectroscopy is now widely practised in soil
sciences. In conjunction with machine learning models, spectral data are used to predict soil …

As a task that aims to assess the trustworthiness of the model's prediction output during
deployment, confidence estimation has received much research attention recently, due to its …

Deep learning techniques have achieved great success in remote sensing image change
detection. Most of them are supervised techniques, which usually require large amounts of …

Pseudo-labelling-based semi-supervised learning (SSL) has demonstrated remarkable
success in enhancing model performance by effectively leveraging a large amount of …

Epistemic uncertainty quantification (UQ) identifies where models lack knowledge.
Traditional UQ methods often based on Bayesian neural networks are not suitable for pre …

Continual learning aims to learn from a stream of continuously arriving data with minimum
forgetting of previously learned knowledge. While previous works have explored the …

Machine learning models are widely used to solve real-world problems in science and
industry. To build robust models, we should quantify the uncertainty of the model's …

Uncertainty quantification is critical for deploying deep neural networks (DNNs) in real-world
applications. An Auxiliary Uncertainty Estimator (AuxUE) is one of the most effective means …