Testing remains the primary method to evaluate the accuracy of neural network perception systems. Prior work on the formal verification of neural network perception models has been limited to notions of local adversarial robustness for classification with respect to individual image inputs. In this work, we propose a notion of global correctness for neural network perception models performing regression with respect to a generative neural network with a semantically meaningful latent space. That is, against an infinite set of images produced by a generative model over an interval of its latent space, we employ neural network verification to prove that the model will always produce estimates within some error bound of the ground truth. Where the perception model fails, we obtain semantically meaningful counter-examples which carry information on concrete states of the system of interest that can be used programmatically without human inspection of corresponding generated images. Our approach, Generate and Verify, provides a new technique to gather insight into the failure cases of neural network perception systems and provide meaningful guarantees of correct behavior in safety critical applications.
On the notion of missingness for path attribution explainability methods in medical settings: Guiding the selection of medically meaningful baselines
Research Group MITI, Technical University of Munich（慕尼黑技术大学MITI研究组）；Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital（慕尼黑技术大学人工智能在医疗健康中的chair及慕尼黑技术大学医院）；Department of Computing, Imperial College London（伦敦帝国理工学院计算机系）；Department of Surgery, Technical University of Munich（慕尼黑技术大学外科系）
A Sim2Real Deep Learning Approach for the Transformation of Images from Multiple Vehicle-Mounted Cameras to a Semantically Segmented Image in Bird's Eye View
