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

Before deploying machine learning models it is critical to assess theirrobustness. In the context of deep neural networks for image understanding,changing the object location, rotation and size may affect the predictions innon-trivial ways. In this work we perform a fine-grained analysis of robustnesswith respect to these factors of variation using SI-Score, a synthetic dataset.In particular, we investigate ResNets, Vision Transformers and CLIP, andidentify interesting qualitative differences between these.

SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size