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

Neural Collapse (NC) gives a precise description of the representations ofclasses in the final hidden layer of classification neural networks. Thisdescription provides insights into how these networks learn features andgeneralize well when trained past zero training error. However, to date, (NC)has only been studied in the final layer of these networks. In the presentpaper, we provide the first comprehensive empirical analysis of the emergenceof (NC) in the intermediate hidden layers of these classifiers. We examine avariety of network architectures, activations, and datasets, and demonstratethat some degree of (NC) emerges in most of the intermediate hidden layers ofthe network, where the degree of collapse in any given layer is typicallypositively correlated with the depth of that layer in the neural network.Moreover, we remark that: (1) almost all of the reduction in intra-classvariance in the samples occurs in the shallower layers of the networks, (2) theangular separation between class means increases consistently with hidden layerdepth, and (3) simple datasets require only the shallower layers of thenetworks to fully learn them, whereas more difficult ones require the entirenetwork. Ultimately, these results provide granular insights into thestructural propagation of features through classification neural networks.

Neural Collapse in the Intermediate Hidden Layers of Classification Neural Networks