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Related Experiment Video

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Deep Cascade Learning.

Enrique S Marquez, Jonathon S Hare, Mahesan Niranjan

    IEEE Transactions on Neural Networks and Learning Systems
    |July 12, 2018
    PubMed
    Summary

    Deep cascade learning trains deep neural networks efficiently, avoiding vanishing gradients. This novel approach learns better domain-specific features in early layers and offers computational advantages.

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Deep neural networks (DNNs) training often suffers from the vanishing gradient problem.
    • Standard end-to-end training with backpropagation can hinder effective feature learning in early layers.
    • Cascade correlation, a perceptron training method, inspires alternative approaches.

    Purpose of the Study:

    • To introduce and evaluate a novel bottom-up training algorithm for deep neural networks called deep cascade learning.
    • To demonstrate the algorithm's effectiveness on convolutional neural networks (CNNs) for image classification.
    • To compare deep cascade learning with standard end-to-end training regarding feature representation and efficiency.

    Main Methods:

    • Implemented a layered, bottom-up training approach termed deep cascade learning.

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  • Applied the algorithm to convolutional layers within neural network architectures.
  • Conducted empirical evaluations on CIFAR-10 and CIFAR-100 datasets, comparing against end-to-end backpropagation.

    • Main Results:

    • Deep cascade learning circumvents the vanishing gradient problem by training layers sequentially.
    • The approach learns superior, domain-specific feature representations in early network layers compared to end-to-end training.
    • Recognition accuracy improved progressively with added layers, showing discriminative features learned at each stage.

    Conclusions:

    • Deep cascade learning offers a viable alternative to end-to-end training for deep neural networks.
    • The method provides significant computational and memory advantages.
    • Deep cascade learning can serve as an effective pretraining algorithm for enhanced performance.