Mining bias-target Alignment from Voronoi Cells

Deep Neural Networks (DNNs) are known today for their high performance and resilience in many areas of computer vision, such as image classification, semantic segmentation, and object detection, used in areas ranging from selfdriving vehicles to face recognition or surgical guidance. However, it is well known that their tendency to rely heavily on any type of correlation present in the training data exposes them to potential pitfalls [17, 2, 39]: some "spurious correlations" may be mistakenly learned by the DNN. These can take over the role of biases [35]. Learned biases may decrease the generalization of the DNN [17, 2, 25, 30, 4, 9]. For example, if a DNN has learned to distinguish airplanes flying in the sky from boats sailing in the ocean, the model will likely use the background as a base for its classification: detecting it instead of learning the vehicle shape is a much simpler task.