Results 11 to 20 of about 58,206,617 (356)
The Data‐Limited Methods Toolkit (
Methods in Ecology and Evolution, 2018 Abstract A simulation‐based approach known as management strategy evaluation (MSE) is increasingly used by resource managers to identify management procedures that are robust to uncertainties in system dynamics. The majority of global fish populations are data limited and there is large uncertainty over their population and exploitation dynamics ...
Thomas R. Carruthers, Adrian R. Hordyk
openaire +2 more sources
Training Strategies for Radiology Deep Learning Models in Data-limited Scenarios. [PDF]
Radiol Artif Intell, 2021 Data-driven approaches have great potential to shape future practices in radiology. The most straightforward strategy to obtain clinically accurate models is to use large, well-curated and annotated datasets. However, patient privacy constraints, tedious
Candemir S +3 more
europepmc +2 more sources
Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery. [PDF]
Adv Drug Deliv RevA critical bottleneck limiting the potential of Machine Learning (ML) and Deep Learning (DL) models within the drug discovery and development (DDD) pipeline is the scarcity of high-quality experimental data. Limited data is not an anomaly but an inherent
Pallikkavaliyaveetil N +1 more
europepmc +2 more sources
KD-DLGAN: Data Limited Image Generation via Knowledge Distillation [PDF]
Computer Vision and Pattern Recognition, 2023 Generative Adversarial Networks (GANs) rely heavily on large-scale training data for training high-quality image generation models. With limited training data, the GAN discriminator often suffers from severe overfitting which directly leads to degraded ...
Kaiwen Cui +5 more
semanticscholar +1 more source
Perspective Flow Aggregation for Data-Limited 6D Object Pose Estimation [PDF]
European Conference on Computer Vision, 2022 Most recent 6D object pose estimation methods, including unsupervised ones, require many real training images. Unfortunately, for some applications, such as those in space or deep under water, acquiring real images, even unannotated, is virtually ...
Yinlin Hu, P. Fua, M. Salzmann
semanticscholar +1 more source
Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases [PDF]
European Conference on Computer Vision, 2020 When the training data are maliciously tampered, the predictions of the acquired deep neural network (DNN) can be manipulated by an adversary known as the Trojan attack (or poisoning backdoor attack). The lack of robustness of DNNs against Trojan attacks
Ren Wang +5 more
semanticscholar +1 more source
Continual-Learning-of-Generative-Models-with-Limited-Data [PDF]
, 2023 This repository contains the implementation of the paper ``Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence.''
Mehmet Dedeoglu (14644163)
core +1 more source
Conservation Science and Practice, 2022 The pelagic thresher shark is among the most heavily exploited shark species in the commercial fisheries of the tropical Indo‐Pacific oceans. Despite this severe exploitation, little is known about pelagic thresher population dynamics, particularly the ...
Wen-Pei Tsai, Chia‐Han Huang
semanticscholar +1 more source
Using GIS and stakeholder involvement to innovate marine mammal bycatch risk assessment in data-limited fisheries. [PDF]
PLoS One, 2020 Fisheries bycatch has been identified as the greatest threat to marine mammals worldwide. Characterizing the impacts of bycatch on marine mammals is challenging because it is difficult to both observe and quantify, particularly in small-scale fisheries ...
Verutes GM +8 more
europepmc +2 more sources
Frontiers in Earth Science, 2023 Determining the eruption frequency-Magnitude (f-M) relationship for data-limited volcanoes is challenging since it requires a comprehensive eruption record of the past eruptive activity.
Vanesa Burgos +11 more
doaj +1 more source