These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.
Out-of-distribution (OOD) generalization
Robustness Metrics provides lightweight modules in order to evaluate the robustness of classification models. OOD generalization is defined as, e.g. a non-expert human would be able to classify similar objects, but possibly changed viewpoint, scene setting or clutter.
The library includes popular out-of-distribution datasets (ImageNetV2, ImageNet-C, etc.) and can be readily applied to benchmark arbitrary models and is not limited to vision models: any mapping from input -> logits will do.
Related use cases :
Towards Out-Of-Distribution Generalization: A Survey
Uploaded on Oct 25, 2022Classic machine learning methods are built on the i.i.d. assumption that training and testing data are independent and identically distributed. However, in real scena...
About the metric
You can click on the links to see the associated metrics
Objective(s):
Lifecycle stage(s):
Target users:
