To deal with the domain shift between training and test samples, current methods have
primarily focused on learning generalizable features during training and ignore the …

Abstract Domain Generalization (DG) techniques have emerged as a popular approach to
address the challenges of domain shift in Deep Learning (DL), with the goal of generalizing …

Artificial Intelligence of Things (AIoT) is an emerging frontier based on the deep fusion of
Internet of Things (IoT) and Artificial Intelligence (AI) technologies. The fundamental goal of …

Abstract Domain generalization (DG) aims to learn a model that generalizes well to unseen
target domains utilizing multiple source domains without re-training. Most existing DG works …

Generalizing visual recognition models trained on a single distribution to unseen input
distributions (ie domains) requires making them robust to superfluous correlations in the …

Deep neural networks (DNNs) usually fail to generalize well to outside of distribution (OOD)
data, especially in the extreme case of single domain generalization (single-DG) that …

Abstract Domain shift across crowd data severely hinders crowd counting models to
generalize to unseen scenarios. Although domain adaptive crowd counting approaches …

Abstract Model quantization, which aims to compress deep neural networks and accelerate
inference speed, has greatly facilitated the development of cumbersome models on mobile …

While machine learning models rapidly advance the state-of-the-art on various real-world
tasks, out-of-domain (OOD) generalization remains a challenging problem given the …

Machine learning models rely on various assumptions to attain high accuracy. One of the
preliminary assumptions of these models is the independent and identical distribution, which …