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/

Zendy Plus

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

Zendy Tools

Zendy Open

While deep learning has led to huge progress in complex image classificationtasks like ImageNet, unexpected failure modes, e.g. via spurious features, callinto question how reliably these classifiers work in the wild. Furthermore, forsafety-critical tasks the black-box nature of their decisions is problematic,and explanations or at least methods which make decisions plausible are neededurgently. In this paper, we address these problems by generating images thatoptimize a classifier-derived objective using a framework for guided imagegeneration. We analyze the decisions of image classifiers by visualcounterfactual explanations (VCEs), detection of systematic mistakes byanalyzing images where classifiers maximally disagree, and visualization ofneurons and spurious features. In this way, we validate existing observations,e.g. the shape bias of adversarially robust models, as well as novel failuremodes, e.g. systematic errors of zero-shot CLIP classifiers. Moreover, our VCEsoutperform previous work while being more versatile.

DiG-IN: Diffusion Guidance for Investigating Networks -- Uncovering  Classifier Differences Neuron Visualisations and Visual Counterfactual  Explanations