Generalizing to unseen domains: A survey on domain generalization
Machine learning systems generally assume that the training and testing distributions are
the same. To this end, a key requirement is to develop models that can generalize to unseen …
the same. To this end, a key requirement is to develop models that can generalize to unseen …
Trustworthy representation learning across domains
As AI systems have obtained significant performance to be deployed widely in our daily live
and human society, people both enjoy the benefits brought by these technologies and suffer …
and human society, people both enjoy the benefits brought by these technologies and suffer …
A Survey of Trustworthy Representation Learning Across Domains
As AI systems have obtained significant performance to be deployed widely in our daily live
and human society, people both enjoy the benefits brought by these technologies and suffer …
and human society, people both enjoy the benefits brought by these technologies and suffer …
Subsidiary prototype alignment for universal domain adaptation
Abstract Universal Domain Adaptation (UniDA) deals with the problem of knowledge transfer
between two datasets with domain-shift as well as category-shift. The goal is to categorize …
between two datasets with domain-shift as well as category-shift. The goal is to categorize …
Agi for agriculture
Artificial General Intelligence (AGI) is poised to revolutionize a variety of sectors, including
healthcare, finance, transportation, and education. Within healthcare, AGI is being utilized to …
healthcare, finance, transportation, and education. Within healthcare, AGI is being utilized to …
Cross-domain graph convolutions for adversarial unsupervised domain adaptation
Unsupervised domain adaptation (UDA) has attracted increasing attention in recent years,
which adapts classifiers to an unlabeled target domain by exploiting a labeled source …
which adapts classifiers to an unlabeled target domain by exploiting a labeled source …
Universal domain adaptive network embedding for node classification
Cross-network node classification aims to leverage the abundant knowledge from a labeled
source network to help classify the node in an unlabeled target network. However, existing …
source network to help classify the node in an unlabeled target network. However, existing …
From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image Segmentation
In medical image segmentation, domain generalization poses a significant challenge due to
domain shifts caused by variations in data acquisition devices and other factors. These shifts …
domain shifts caused by variations in data acquisition devices and other factors. These shifts …
Pairwise adversarial training for unsupervised class-imbalanced domain adaptation
Unsupervised domain adaptation (UDA) has become an appealing approach for knowledge
transfer from a labeled source domain to an unlabeled target domain. However, when the …
transfer from a labeled source domain to an unlabeled target domain. However, when the …
Progressive mix-up for few-shot supervised multi-source domain transfer
This paper targets at a new and challenging setting of knowledge transfer from multiple
source domains to a single target domain, where target data is few shot or even one shot …
source domains to a single target domain, where target data is few shot or even one shot …