English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Deep learning models extract, before a final classification layer, featuresor patterns which are key for their unprecedented advantageous performance.However, the process of complex nonlinear feature extraction is not wellunderstood, a major reason why interpretation, adversarial robustness, andgeneralization of deep neural nets are all open research problems. In thispaper, we use wavelet transformation and spectral methods to analyze thecontents of image classification datasets, extract specific patterns from thedatasets and find the associations between patterns and classes. We show thateach image can be written as the summation of a finite number of rank-1patterns in the wavelet space, providing a low rank approximation that capturesthe structures and patterns essential for learning. Regarding the studies onmemorization vs learning, our results clearly reveal disassociation of patternsfrom classes, when images are randomly labeled. Our method can be used as apattern recognition approach to understand and interpret learnability of thesedatasets. It may also be used for gaining insights about the features andpatterns that deep classifiers learn from the datasets.

Using Wavelets and Spectral Methods to Study Patterns in Image-Classification Datasets