Addressing annotation and data scarcity when designing machine ...

We present an overview of various strategies, such as weakly supervised learning, active learning, and domain adaptation that can be used to address the ...

Machine learning has revolutionized the way data are processed, allowing information to be extracted in a fraction of the time it would take an expert.

(PDF) Addressing annotation and data scarcity when designing ...

The approaches we describe can help to improve the accessibility of machine learning-based analysis with limited number of annotated images for ...

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Addressing annotation and data scarcity when designing machine ...

Addressing annotation and data scarcity when designing machine learning strategies for neurophotonics. https://doi.org/10.1117/1.nph.10.4.044405 ·. Journal ...

Publications | Page 48 | Cervo Brain Research Centre

“Addressing annotation and data scarcity when designing machine learning strategies for neurophotonics.”, Neurophotonics, vol. 10, no. 4, p. 044405, 2023.

Addressing annotation and data scarcity when designing machine ...

Article "Addressing annotation and data scarcity when designing machine learning strategies for neurophotonics" Detailed information of the J-GLOBAL is an ...

How to overcome data scarcity with quality data labeling

Low-quality labeling impairs the performance and reliability of ML models. Inaccurate annotations can trick AI models into learning from ...

Addressing the Data Scarcity Issue of Supervised Machine Learning ...

This issue is particularly important in the social sciences where most new research questions require new training data for a new task tailored ...

Less Annotating, More Classifying: Addressing the Data Scarcity ...

Less Annotating, More Classifying: Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and ...

Addressing Data Scarcity and Label Skew in Federated Learning via ...

The above analysis highlights the challenge of overfitting caused by data scarcity, and further suggests that globally sharing certain ...

Addressing annotation and data scarcity when designing machine ...

Addressing annotation and data scarcity when designing machine learning strategies for neurophotonics. Machine learning has revolutionized the way data are ...

Designing/Scaling ML/AI Systems in when labelled data is scarce ...

Today I am going to talk about a very important topic of how to design/scale ML/AI systems when labelled data is not available or labelled ...

Revolutionizing Machine Learning: The Role of Data Annotation in ...

Mitigation of Data Scarcity: ... Accumulating extensive amounts of labeled data for specific tasks presents significant challenges, involving both ...

A survey on deep learning tools dealing with data scarcity

This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data ...

Addressing Data Scarcity with Computer Vision Methods

Lack of quality data is a significant issue when designing and implementing computer vision-based algorithms; more specifically, deep learning-based approaches ...

Addressing data scarcity in protein fitness landscape analysis

While deep learning and deep transfer learning methods have shown remarkable performance in situations with abundant data, this study aims to address the more ...

Less Annotating, More Classifying – Addressing the Data Scarcity ...

This paper argues that this data scarcity problem of supervised machine learning can be mitigated through deep transfer learning. The main assumption of ...

Data Labeling Challenges and Solutions - DATAVERSITY

Accurate data labeling and annotation are crucial for reliable machine learning systems, but applying complex ontologies is time-consuming.

Utilising physics-guided deep learning to overcome data scarcity

... annotation and curation of high-quality data ... Therefore, with these established principles and laws, PGDL can be an effective solution to address the data ...