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Feedforward neural networks initialization based on discriminant learning.

Kateryna Chumachenko1, Alexandros Iosifidis2, Moncef Gabbouj1

  • 1Faculty of Information Technology and Communication Sciences, Tampere University, FI 33720, Tampere, Finland.

Neural Networks : the Official Journal of the International Neural Network Society
|December 13, 2021
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Summary
This summary is machine-generated.

A new data-driven method for initializing Multilayer Perceptrons and Convolutional Neural Networks improves model accuracy and convergence. This approach overcomes limitations of existing methods, offering better performance on large-scale image datasets.

Keywords:
Discriminant learningNeural networks initialization

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Area of Science:

  • Machine Learning
  • Deep Learning
  • Computer Vision

Background:

  • Weight initialization is crucial for training deep neural networks like Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs).
  • Existing data-driven initialization methods have limitations, including unimodality assumptions and high computational costs.

Purpose of the Study:

  • To propose a novel data-driven weight initialization method for MLPs and CNNs based on discriminant learning.
  • To address limitations of current methods, such as architectural constraints and computational demands.

Main Methods:

  • Developed a discriminant learning-based approach for weight initialization.
  • Introduced a new normalization layer to handle data assumptions.
  • Evaluated the method on three large-scale image datasets.

Main Results:

  • The proposed method demonstrated improved accuracy compared to random and other data-driven initialization techniques.
  • Observed enhanced convergence properties in specific experimental scenarios.
  • Successfully trained models without strict unimodality assumptions or high-dimensional eigendecomposition.

Conclusions:

  • The novel data-driven initialization method offers a more robust and efficient alternative for training deep neural networks.
  • The approach effectively improves model performance and training dynamics, particularly for image-related tasks.