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

Various saliency map methods have been proposed to interpret and explainpredictions of deep learning models. Saliency maps allow us to interpret whichparts of the input signals have a strong influence on the prediction results.However, since a saliency map is obtained by complex computations in deeplearning models, it is often difficult to know how reliable the saliency mapitself is. In this study, we propose a method to quantify the reliability of asalient region in the form of p-values. Our idea is to consider a salientregion as a selected hypothesis by the trained deep learning model and employthe selective inference framework. The proposed method can provably control theprobability of false positive detections of salient regions. We demonstrate thevalidity of the proposed method through numerical examples in synthetic andreal datasets. Furthermore, we develop a Keras-based framework for conductingthe proposed selective inference for a wide class of CNNs without additionalimplementation cost.

Valid P-Value for Deep Learning-Driven Salient Region