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A Data-Centric Approach to improve performance of deep learning models.

Nikita Bhatt1, Nirav Bhatt2, Purvi Prajapati3

  • 1Department of Computer Engineering, U & P U. Patel, CSPIT, CHARUSAT, Changa, Gujarat, India.

Scientific Reports
|September 27, 2024
PubMed
Summary
This summary is machine-generated.

The Data-Centric Approach, focusing on high-quality data, outperforms the traditional Model-Centric Approach in deep learning. This data-oriented technique shows significant performance gains, highlighting the importance of data quality for AI advancements.

Keywords:
Data Centric ApproachDeep learningHyper parameter tuningModel Centric Approach

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Area of Science:

  • Artificial Intelligence
  • Deep Learning
  • Data Science

Background:

  • The evolution of Artificial Intelligence (AI) is increasingly tied to Deep Learning, fueled by massive datasets and computational power.
  • Traditionally, a Model-Centric Approach dominated AI research, prioritizing algorithm development over data quality.
  • A shift towards a Data-Centric Approach, emphasizing high-quality data, is gaining momentum, spurred by experts like Andrew Ng.

Purpose of the Study:

  • To address the challenge of generating high-quality data in the Data-Centric Approach.
  • To investigate the effectiveness of data-centric methods compared to model-centric methods in deep learning.
  • To explore the potential of data-centric AI in various application domains.

Main Methods:

  • Implemented data augmentation techniques to enhance dataset quality.
  • Utilized multi-stage hashing to identify and remove duplicate data instances.
  • Employed confident learning for detecting and correcting noisy labels in datasets.
  • Conducted experiments using ResNet-18 on MNIST, Fashion MNIST, and CIFAR-10 datasets.

Main Results:

  • The Data-Centric Approach consistently outperformed the Model-Centric Approach.
  • A performance improvement of at least 3% was observed with the data-centric method.
  • The study demonstrated the practical benefits of prioritizing data quality in deep learning models.

Conclusions:

  • The Data-Centric Approach offers a significant advantage over the Model-Centric Approach in deep learning.
  • High-quality data generation is crucial for advancing AI performance.
  • The findings support the broader adoption of data-centric strategies across diverse fields like healthcare, finance, and education.