Interactive Analysis of CNN Robustness

  title={Interactive Analysis of CNN Robustness},
  author={Stefan Sietzen and Mathias Lechner and Judy Borowski and Ramin M. Hasani and Manuela Waldner},
  journal={Computer Graphics Forum},
While convolutional neural networks (CNNs) have found wide adoption as state‐of‐the‐art models for image‐related tasks, their predictions are often highly sensitive to small input perturbations, which the human vision is robust against. This paper presents Perturber, a web‐based application that allows users to instantaneously explore how CNN activations and predictions evolve when a 3D input scene is interactively perturbed. Perturber offers a large variety of scene modifications, such as… 
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