The world we see is ever-changing and it always changes with people, things, and the
environment. Domain is referred to as the state of the world at a certain moment. A research …

This article takes a problem-oriented perspective and presents a comprehensive review of
transfer-learning methods, both shallow and deep, for cross-dataset visual recognition …

Few-shot classification aims to learn a classifier to recognize unseen classes during training
with limited labeled examples. While significant progress has been made, the growing …

Fine-tuning a deep network trained with the standard cross-entropy loss is a strong baseline
for few-shot learning. When fine-tuned transductively, this outperforms the current state-of …

Data-driven approaches for prognostic and health management (PHM) increasingly rely on
massive historical data, yet annotations are expensive and time-consuming. Learning …

Deep domain adaptation has emerged as a new learning technique to address the lack of
massive amounts of labeled data. Compared to conventional methods, which learn shared …

In this paper, we propose a new unsupervised domain adaptation approach called
Collaborative and Adversarial Network (CAN) through domain-collaborative and domain …

In this paper, we propose an elegant solution that is directly addressing the bottlenecks of
the traditional deep learning approaches and offers an explainable internal architecture that …

Person re-identification (re-ID) poses unique challenges for unsupervised domain
adaptation (UDA) in that classes in the source and target sets (domains) are entirely different …

The accurate identification of malignant lung nodules on chest CT is critical for the early
detection of lung cancer, which also offers patients the best chance of cure. Deep learning …