Deep Learning Techniques Addressing Data Scarcity

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 ...

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

The approach employs Generative Adversarial Networks to generate synthetic data and LSTM layers to extract temporal features. ML algorithms ...

Current strategies to address data scarcity in artificial intelligence ...

These are transferring learning (TL), active learning (AL), single or one-shot learning (OSL), multi-task learning (MTL), data augmentation (DA), data synthesis ...

Tackling Data Scarcity in Deep Learning - Caltech

Strategies to Cope with Scarce Data. • Data Augmentation. • Semi-supervised Learning. • Transfer Learning. • Domain Adaptation. • Active Learning. Page 9 ...

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 ...

Utilising physics-guided deep learning to overcome data scarcity

promising machine learning techniques in recent decades. ... Physics-guided deep learning can address the challenge of data scarcity in engineering by.

A Comprehensive List Of Proven Techniques To Address Data ...

A lot of AI/ML ideas, in very early stages, face significant roadblocks due to Data Scarcity, i.e., no data or low data.

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 ...

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 ...

Solve Data Scarcity For Using of Artificial Intelligence - glair.ai

Machine learning is an inductive process, so it's best to incorporate edge cases in the training dataset to avoid model failure and inaccuracy. Some ...

(PDF) 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 ...

A Systematic Review on Data Scarcity Problem in Deep Learning

The purpose of this study focuses on various data augmentation techniques that can be used to further improve the accuracy of a neural network.

Addressing Data Scarcity In Your AI Journey - Thought Leadership

We know that Machine Learning (ML) and especially Deep Learning (DL) require BIG data to work well. While companies like Amazon and Google ...

[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, ...

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 ...

Navigating Data Scarcity in AI: Implications for AGI Development

... data challenge? This article delves into how existing methods of addressing data scarcity in machine learning (ML) might be adapted for AGI ...

Current strategies to address data scarcity in artificial intelligence ...

This development has been further accelerated with the increasing use of machine learning (ML), mainly deep learning (DL), and computing ...

Data Scarcity | Deepgram

Labeled data, essential for training machine learning models, is often scarce and expensive to produce. The scarcity of labeled data versus the ...

Addressing Data Scarcity in Solar Energy Prediction with Machine ...

Advanced machine learning techniques, including random forest (RF) and extreme gradient boosting (XGBoost) regressors, are employed to effectively predict GHI.

Mitigating Data Scarcity in Supervised Machine Learning Through ...

Abstract: One primary problem for supervised ML is data scarcity, which refers to the inadequacy of well-labeled training data. Recently, deep generative ...