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

Model calibration, which is concerned with how frequently the model predictscorrectly, not only plays a vital part in statistical model design, but alsohas substantial practical applications, such as optimal decision-making in thereal world. However, it has been discovered that modern deep neural networksare generally poorly calibrated due to the overestimation (or underestimation)of predictive confidence, which is closely related to overfitting. In thispaper, we propose Annealing Double-Head, a simple-to-implement but highlyeffective architecture for calibrating the DNN during training. To be precise,we construct an additional calibration head-a shallow neural network thattypically has one latent layer-on top of the last latent layer in the normalmodel to map the logits to the aligned confidence. Furthermore, a simpleAnnealing technique that dynamically scales the logits by calibration head intraining procedure is developed to improve its performance. Under both thein-distribution and distributional shift circumstances, we exhaustivelyevaluate our Annealing Double-Head architecture on multiple pairs ofcontemporary DNN architectures and vision and speech datasets. We demonstratethat our method achieves state-of-the-art model calibration performance withoutpost-processing while simultaneously providing comparable predictive accuracyin comparison to other recently proposed calibration methods on a range oflearning tasks.

Annealing Double-Head: An Architecture for Online Calibration of Deep Neural Networks