Sensor techniques and emerging CNN models have greatly facilitated the development of
collaborative fault diagnosis. Existing CNN models apply different fusion schemes to …

Abstract Knowledge Distillation (KD) uses the teacher's prediction logits as soft labels to
guide the student, while self-KD does not need a real teacher to require the soft labels. This …

In this work, we study the continual semantic segmentation problem, where the deep neural
networks are required to incorporate new classes continually without catastrophic forgetting …

Abstract Knowledge distillation (KD) shows a bright promise as a powerful regularization
strategy to boost generalization ability by leveraging learned sample-level soft targets. Yet …

Measuring the phenotypic effect of treatments on cells through imaging assays is an efficient
and powerful way of studying cell biology, and requires computational methods for …

Convolutional neural network (CNN)-based fault detection approaches based on
multisource signals have attracted increasing interest from the research community and …

The labels used to train machine learning (ML) models are of paramount importance.
Typically for ML classification tasks, datasets contain hard labels, yet learning using soft …

In the era of deep learning, loss functions determine the range of tasks available to models
and algorithms. To support the application of deep learning in multi-label classification …

Most existing methods that cope with noisy labels usually assume that the classwise data
distributions are well balanced. They are difficult to deal with the practical scenarios where …

Objective. Steady-state visual evoked potentials (SSVEPs) based brain–computer interface
(BCI) has received great interests owing to the high information transfer rate and available …