Results 11 to 20 of about 2,154,051 (288)
Climate Change 2022 – Impacts, Adaptation and Vulnerability
, 2023 The Working Group II contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) provides a comprehensive assessment of the scientific literature relevant to climate change impacts, adaptation and vulnerability ...
Dr. Kirstin K. Holsman
semanticscholar +1 more source
Adversarial Discriminative Domain Adaptation [PDF]
Computer Vision and Pattern Recognition, 2017 Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains. They can also improve recognition despite the presence of domain shift or dataset bias: recent adversarial ...
E. Tzeng +3 more
semanticscholar +1 more source
Continual Test-Time Domain Adaptation [PDF]
Computer Vision and Pattern Recognition, 2022 Test-time domain adaptation aims to adapt a source pre-trained model to a target domain without using any source data. Existing works mainly consider the case where the target domain is static.
Qin Wang +3 more
semanticscholar +1 more source
Moment Matching for Multi-Source Domain Adaptation [PDF]
IEEE International Conference on Computer Vision, 2018 Conventional unsupervised domain adaptation (UDA) assumes that training data are sampled from a single domain. This neglects the more practical scenario where training data are collected from multiple sources, requiring multi-source domain adaptation. We
Xingchao Peng +5 more
semanticscholar +1 more source
Contrastive Test-Time Adaptation [PDF]
Computer Vision and Pattern Recognition, 2022 Test-time adaptation is a special setting of unsupervised domain adaptation where a trained model on the source domain has to adapt to the target domain without accessing source data.
Dian Chen +3 more
semanticscholar +1 more source
Deep Hashing Network for Unsupervised Domain Adaptation [PDF]
Computer Vision and Pattern Recognition, 2017 In recent years, deep neural networks have emerged as a dominant machine learning tool for a wide variety of application domains. However, training a deep neural network requires a large amount of labeled data, which is an expensive process in terms of ...
Hemanth Venkateswara +3 more
semanticscholar +1 more source
OPTIONS FOR ADAPTATION OF URBANIZED AREAS TO CLIMATE CHANGE INDUCED EXTREME RAINFALLS [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 The impacts of the climate change progress in a form of heavy cloudbursts, have been being reported over the course of last decade from many world cities.
T. Metelka, T. Janu, J. Sebek
doaj +1 more source
FDA: Fourier Domain Adaptation for Semantic Segmentation [PDF]
Computer Vision and Pattern Recognition, 2020 We describe a simple method for unsupervised domain adaptation, whereby the discrepancy between the source and target distributions is reduced by swapping the low-frequency spectrum of one with the other. We illustrate the method in semantic segmentation,
Yanchao Yang, Stefano Soatto
semanticscholar +1 more source
Feeding behavior in relation to ovarian cyclicity in cows with no or a short dry period
Journal of Dairy Science, 2023 : The objective of this study was to investigate relations between feeding behavior (FB) variables focusing on intake of the basal ration during 3 wk prepartum until 3 wk postpartum, and postpartum onset of luteal activity (OLA) in dairy cows subjected ...
B.G.C. de Bruijn +4 more
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Maximum Classifier Discrepancy for Unsupervised Domain Adaptation [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 In this work, we present a method for unsupervised domain adaptation. Many adversarial learning methods train domain classifier networks to distinguish the features as either a source or target and train a feature generator network to mimic the ...
Kuniaki Saito +3 more
semanticscholar +1 more source