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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In the classical supervised learning settings, classifiers are fit with theassumption of balanced label distributions and produce remarkable results onthe same. In the real world, however, these assumptions often bend and in turnadversely impact model performance. Identifying bad learners in skewed targetdistributions is even more challenging. Thus achieving model robustness underthese "label shift" settings is an important task in autonomous perception. Inthis paper, we analyze the impact of label shift on the task of multi-weatherclassification for autonomous vehicles. We use this information as a prior tobetter assess pedestrian detection in adverse weather. We model theclassification performance as an indicator of robustness under 4 label shiftscenarios and study the behavior of multiple classes of models. We proposet-RAIN a similarity mapping technique for synthetic data augmentation usinglarge scale generative models and evaluate the performance on DAWN dataset.This mapping boosts model test accuracy by 2.1, 4.4, 1.9, 2.7 % in no-shift,fog, snow, dust shifts respectively. We present state-of-the-art pedestriandetection results on real and synthetic weather domains with best performing82.69 AP (snow) and 62.31 AP (fog) respectively.

t-RAIN: Robust generalization under weather-aliasing label shift attacks