Tackling Data Scarcity in Deep Learning

A survey on deep learning tools dealing with data scarcity

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional ...

Tackling Data Scarcity in Deep Learning - Caltech

Tackling Data Scarcity in Deep Learning. Anima Anandkumar & Zachary Lipton ... • Deep Active Learning for Named Entity Recognition (ICLR 2018) https ...

Strategies for overcoming data scarcity, imbalance, and feature ...

The use of machine learning algorithms in data analytics has seen rapid growth over the recent past. In PdM, such algorithms enable the analysis ...

GTC-DC 2019: Tackling Data Scarcity and Bias in Deep Learning

GTC-DC 2019: Tackling Data Scarcity and Bias in Deep Learning ... We'll explain how to alleviate the difficulty of obtaining large labeled datasets that are often ...

Overcoming Data Scarcity in Deep Learning of Scientific Problems

An essential criteria to ensure the success of such methods is the need for extensive amounts of labeled data, making it unfeasible for data-scarce problems ...

GTC-DC 2019: Tackling Data Scarcity and Bias in | NVIDIA Developer

We'll explain how to alleviate the difficulty of obtaining large labeled datasets that are often required for training in modern deep learning.

Tackling Data Scarcity Challenge through Active Learning in ...

Machine learning (ML) has been harnessed as a promising modelling tool for materials research. However, small data, or data scarcity, ...

Utilising physics-guided deep learning to overcome data scarcity

Deep learning has been widely used in the field of engineering to tackle a variety of tasks37,38, but data scarcity can present a challenge when it comes to ...

Dealing with Data Scarcity in Artificial Intelligence - Infosys Blogs

A few machine learning algorithms inherently need a smaller sample size as compared to the others. Non-linear algorithms generally require a comparatively ...

A Systematic Review on Data Scarcity Problem in Deep Learning

Data augmentation helps in solving the problem of data scarcity, but there are also other methods that deal with the issue of limited ...

[PDF] A survey on deep learning tools dealing with data scarcity

This paper presents a holistic survey on state-of-the-art techniques to deal with training DL models to overcome three challenges including small, ...

Handling Data Scarcity while building Machine Learning applications

One way to tackle the lack of labelled data is to artificially manufacture synthetic data for your specific problem. Synthetic data is used ...

Mitigating Data Scarcity in Supervised Machine Learning Through ...

Naturally, a promising approach for tackling data scarcity involves training a generative model to produce a collection of data objects, and then employing ...

(PDF) A survey on deep learning tools dealing with data scarcity

Having sufficient data is the first step toward any successful and trustworthy DL application. This paper presents a holistic survey on state-of ...

How to overcome data scarcity with quality data labeling

This is invaluable for tackling complex or niche tasks where automated systems may not capture the subtleties needed in sparse datasets, thereby ...

Overcoming Data Scarcity in AI Model Training | Medium

Transfer Learning and Few-Shot Learning address data scarcity by leveraging pre-trained models and minimal labeled data to enhance AI ...

Synthetic data is the breakthrough you need to tackle data scarcity

Data scarcity refers to the inadequacy of available data volume required for meaningful analysis or effective training of AI and machine learning models. It's ...

How to Deal With the Lack of Data in Machine Learning - BroutonLab

Training data shortage represents a crucial issue, also because if AI hesitates about the result, it won't signalize to show its uncertainty but will complete ...

David Acuna - Overcoming Data Scarcity in Deep Learning - YouTube

May 28th, 2020. MIT CSAIL Abstract Training deep neural networks for computer vision tasks is a time-consuming and expensive process that ...

Ten deep learning techniques to address small data problems with ...

A relatively small dataset can negatively affect the performance of a DL model due to overfitting, which is when a model performs well with the training data ...