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Forward layer-wise learning of convolutional neural networks through separation index maximizing.

Ali Karimi1, Ahmad Kalhor2, Melika Sadeghi Tabrizi1

  • 1School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.

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Summary

This study introduces a novel forward layer-wise learning algorithm for Convolutional Neural Networks (CNNs). It enhances classification accuracy and efficiency by optimizing layer complexity using the Separation Index (SI).

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional deep learning algorithms often rely on backpropagation, which can be computationally intensive and may struggle with vanishing gradients.
  • Layer-wise training offers a potential alternative for optimizing complex neural network architectures, but effective methods are still under development.

Purpose of the Study:

  • To propose a novel forward layer-wise learning algorithm for Convolutional Neural Networks (CNNs) in classification tasks.
  • To introduce the Separation Index (SI) as a supervised complexity measure for evaluating and training individual network layers.
  • To demonstrate improved feature representation by reducing data uncertainties and disturbances through a forward, layer-by-layer approach.

Main Methods:

  • A forward layer-wise learning algorithm is proposed, utilizing the Separation Index (SI) to assess and train each layer sequentially.
  • The SI is approximated using a variant of local triplet loss, enabling gradient-based maximization to enhance feature representation.
  • The algorithm operates in a forward pass, inspired by the Neural Gradient Representation by Activity Differences (NGRAD) hypothesis, without requiring explicit error signals from the final layer.

Main Results:

  • The proposed algorithm achieved superior performance in terms of accuracy compared to state-of-the-art methods across various image classification datasets (CIFAR-10, CIFAR-100, Raabin-WBC, Fashion-MNIST) and architectures (VGG16, VGG19, AlexNet, LeNet).
  • Evaluations on text classification tasks (DBPedia, AG's News) also showed significant improvements.
  • The method demonstrated enhanced time complexity, indicating greater efficiency.

Conclusions:

  • The forward layer-wise learning algorithm effectively optimizes CNNs for classification by improving feature space representation.
  • The Separation Index (SI) serves as a viable complexity measure for layer-wise training, outperforming existing methods.
  • This approach offers a computationally efficient and accurate alternative for training deep neural networks.