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

The capability of Deep Neural Networks (DNNs) to recognize objects inorientations outside the distribution of the training data is not wellunderstood. We present evidence that DNNs are capable of generalizing toobjects in novel orientations by disseminating orientation-invariance obtainedfrom familiar objects seen from many viewpoints. This capability strengthenswhen training the DNN with an increasing number of familiar objects, but onlyin orientations that involve 2D rotations of familiar orientations. We showthat this dissemination is achieved via neurons tuned to common featuresbetween familiar and unfamiliar objects. These results implicate brain-likeneural mechanisms for generalization.

Emergent Neural Network Mechanisms for Generalization to Objects in Novel Orientations