Additive manufacturing (AM) provides a data-rich environment for collecting a variety of
process data. These crucial data can be used to develop effective machine learning (ML) …

Continual learning, also known as incremental learning or life-long learning, stands at the
forefront of deep learning and AI systems. It breaks through the obstacle of one-way training …

Continual semantic segmentation (CSS) based on incremental learning (IL) is a great
endeavour in develo** human-like segmentation models. However, current CSS …

In real-world settings, object detectors frequently encounter continuously emerging object
instances from new classes. Incremental Object Detection (IOD) addresses this challenge by …

As a novel paradigm in machine learning, deep transfer learning (DTL) can harness the
strengths of deep learning for feature representation, while also capitalizing on the …

Edge-based intelligent data analytics supports the Industrial Internet of Things (IIoT) to
enable efficient manufacturing. Incremental learning in the edge-based data analytics has …

This paper delves into the verification of Convolutional Neural Networks for the crucial task
of identifying vehicles in automotive images. Given the complexity and verifiability …

Despite significant advances in continual semantic segmentation (CSS), they still rely on the
pixel-level annotation to train models, which is time-consuming and labor-intensive …

Temporal action segmentation (TAS) seeks to perform classification for each frame in a
video. Existing methods tend to design diverse network architectures, while overlooking the …

The degradation of the pretightening state of bolts is a multistage process. Utilising a single
model to monitor the pretightening state in the full degradation stage and continuously …