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Towards a universal mechanism for successful deep learning.

Yuval Meir1, Yarden Tzach1, Shiri Hodassman1

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Deep learning (DL) success relies on filters sharpening features and improving signal-to-noise ratio (SNR) with layers. This mechanism, verified across datasets, enables network accuracy and potential for architecture dilution.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep learning (DL) models' success mechanisms are increasingly studied.
  • A quantitative method previously identified filter quality in DL models.
  • This method linked filter performance to signal-to-noise ratio (SNR) and accuracy.

Purpose of the Study:

  • To verify the filter quality mechanism in VGG-16 and EfficientNet-B0 models.
  • To assess the mechanism's universality across different datasets (CIFAR-100, ImageNet).
  • To explore implications for DL architecture optimization.

Main Methods:

  • Quantitative analysis of single filter quality in DL layers.
  • Training VGG-16 and EfficientNet-B0 on CIFAR-100 and ImageNet datasets.
  • Measuring signal-to-noise ratio (SNR) and accuracy progression through network layers.

Main Results:

  • Accuracy and SNR progressively increased with network layers.
  • Maximal error rate showed a near-linear increase with the number of output labels.
  • Observed trends held across datasets with varying label counts (3 to 1000), supporting mechanism universality.

Conclusions:

  • The filter quality mechanism, enhancing SNR and accuracy, is validated across architectures and datasets.
  • Understanding filter performance aids in optimizing DL architectures, potentially allowing for significant dilution without accuracy loss.
  • The proposed filter's cluster connections (AFCC) offer a method for architecture optimization.