In unsupervised domain adaptation (UDA), directly adapting from the source to the target domain usually suffers significant discrepancies and leads to insufficient alignment. Thus, many UDA works attempt to vanish the domain gap gradually and softly...
Domain shifts are critical issues that harm the performance of machine learning. Unsupervised Domain Adaptation (UDA) mitigates this issue but suffers when the domain shifts are steep and drastic. Gradual Domain Adaptation (GDA) alleviates this probl...
BadBlood by @davidprowe, Secframe.com, fills a Microsoft Active Directory Domain with a structure and thousands of objects. The output of the tool is a domain similar to a domain in the real world. After BadBlood is ran on a domain, security analysts and engin…
We review the status of domain walls in $N=1$ supergravity theories for both the vacuum domain walls as well as dilatonic domain walls. We concentrate on a systematic analysis of the nature of the space-time in such domain wall backgrounds and the...
This study addresses the problem of calibrating network confidence while adapting a model that was originally trained on a source domain to a target domain using unlabeled samples from the target domain. The absence of labels from the target domain m...
We carry out large-scale micromagnetic simulations which demonstrate that due to topological constraints, internal domain walls (Bloch lines) within extended domain walls are more robust than domain walls in nanowires. Thus, the possibility of spintr...
The vast majority of existing algorithms for unsupervised domain adaptation (UDA) focus on adapting from a labeled source domain to an unlabeled target domain directly in a one-off way. Gradual domain adaptation (GDA), on the other hand, assumes a pa...
Domain-driven design (DDD) is a software design approach that focuses on modeling software to match a domain according to input from that domain's experts
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We consider the problem of domain generalization, namely, how to learn representations given data from a set of domains that generalize to data from a previously unseen domain. We propose the Domain Invariant Variational Autoencoder (DIVA), a generat...
Few-shot Test-Time Domain Adaptation focuses on adapting a model at test time to a specific domain using only a few unlabeled examples, addressing domain shift. Prior methods leverage CLIP's strong out-of-distribution (OOD) abilities by generating do...
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While Unsupervised Domain Adaptation (UDA) algorithms, i.e., there are only labeled data from source domains, have been actively studied in recent years, most algorithms and theoretical results focus on Single-source Unsupervised Domain Adaptation (S...
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Domain adaptation refers to the problem of leveraging labeled data in a source domain to learn an accurate model in a target domain where labels are scarce or unavailable. A recent approach for finding a common representation of the two domains is vi...
In molecular biology, binding domain is a protein domain which binds to a specific atom or molecule, such as calcium or DNA. A protein domain is a part