Intrigued by this result, we set out to explore how well diffusion-pretrained representations generalize to new domains, a crucial ability for any representation.
Domain adaptive object detection aims to leverage the knowledge learned from a labeled source domain to improve the performance on an unlabeled target domain.
Unsupervised domain adaptation (UDA) for semantic segmentation aims at improving the model performance on the unlabeled target domain by leveraging a labeled source domain.
Thus, we aim to relieve this need on a large number of real data, and explore the one-shot unsupervised sim-to-real domain adaptation (OSUDA) and generalization (OSDG) problem, where only one real-world data sample is available.
The compression of videos on social media has destroyed some pixel details that could be used to detect forgeries.
In many real-world settings, the target domain task requires a different taxonomy than the one imposed by the source domain.
In detail, we examine a combination of recent contrastive learning methodologies like Momentum Contrast (MoCo) and Cross-Level Instance-Group Discrimination (CLD) to condition our model on the aerial images without the requirement for labels.
To date, very few biomedical signals have transitioned from research applications to clinical applications.
One challenge of object recognition is to generalize to new domains, to more classes and/or to new modalities.
Open compound domain adaptation (OCDA) is a domain adaptation setting, where target domain is modeled as a compound of multiple unknown homogeneous domains, which brings the advantage of improved generalization to unseen domains.
In this paper, we propose a unified SfM method, in which the matching process is supported by self-calibration constraints.
AIT achieves this zero-shot image translation capability by coupling a supervised training scheme in the synthetic domain, a cycle consistency strategy in the real domain, an adversarial training scheme between the two domains, and a novel network design.
no code implementations • 2 Aug 2019 • Henrik Skibbe, Akiya Watakabe, Ken Nakae, Carlos Enrique Gutierrez, Hiromichi Tsukada, Junichi Hata, Takashi Kawase, Rui Gong, Alexander Woodward, Kenji Doya, Hideyuki Okano, Tetsuo Yamamori, Shin Ishii
Understanding the connectivity in the brain is an important prerequisite for understanding how the brain processes information.
In this work, we present a domain flow generation(DLOW) model to bridge two different domains by generating a continuous sequence of intermediate domains flowing from one domain to the other.