An underlying mechanism for successful deep learning (DL) with a limited deep
architecture and dataset, namely VGG-16 on CIFAR-10, was recently presented
based on a quantitative method to measure the quality of a single filter in
each layer. In this method, each filter identifies small clusters of possible
output labels, with additional noise selected as labels out of the clusters.
This feature is progressively sharpened with the layers, resulting in an
enhanced signal-to-noise ratio (SNR) and higher accuracy. In this study, the
suggested universal mechanism is verified for VGG-16 and EfficientNet-B0
trained on the CIFAR-100 and ImageNet datasets with the following main results.
First, the accuracy progressively increases with the layers, whereas the noise
per filter typically progressively decreases. Second, for a given deep
architecture, the maximal error rate increases approximately linearly with the
number of output labels. Third, the average filter cluster size and the number
of clusters per filter at the last convolutional layer adjacent to the output
layer are almost independent of the number of dataset labels in the range [3,
1,000], while a high SNR is preserved. The presented DL mechanism suggests
several techniques, such as applying filter's cluster connections (AFCC), to
improve the computational complexity and accuracy of deep architectures and
furthermore pinpoints the simplification of pre-existing structures while
maintaining their accuracies.Comment: 27 pages,5 figures, 6 tables. arXiv admin note: text overlap with
arXiv:2305.1807