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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 ...
Eric Tzeng +3 more
semanticscholar +3 more sources
A PHOTOMETRIC ADAPTATION OF THE SOMOGYI METHOD FOR THE DETERMINATION OF GLUCOSE
, 1944 Norton Nelson
openalex +2 more sources
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
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
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
Journal of Analytical Methods in Chemistry, 2022 Nucleotide sugars, the activated forms of monosaccharides, are important intermediates of carbohydrate metabolism in all organisms. Here, we describe a method for the detection and quantification of UDP-glucose and UDP-galactose in maize in order to ...
Chen Lan +6 more
doaj +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
Scientific Reports, 2022 Little is known about the impact of social and environmental enrichment on improving livestock resilience, i.e. the ability to quickly recover from perturbations.
S. P. Parois +5 more
doaj +1 more source
Defining adaptation in a generic multi layer model : CAM: the GRAPPLE conceptual adaptation model [PDF]
, 2008 Authoring of Adaptive Hypermedia is a difficult and time consuming task. Reference models like LAOS and AHAM separate adaptation and content in different layers. Systems like AHA!
Bra, Paul M. E. de +4 more
core +4 more sources
Environmental Evidence, 2023 Background Among the most widely predicted climate change-related impacts to biodiversity are geographic range shifts, whereby species shift their spatial distribution to track their climate niches.
Madeleine A. Rubenstein +13 more
doaj +1 more source