Now showing items 1-2 of 2

    • Towards Explainable Generative Adversarial Networks 

      Yu, Xiaozhuo (University of Waterloo, 2022-05-09)
      As Generative Adversarial Networks become more and more popular for sample generation, the demand for human interpretable explanations have also skyrocketed. With the rising popularity of Generative Adversarial Networks ...
    • XC: Exploring Quantitative Use Cases for Explanations in 3D Object Detection 

      Gu, Sunsheng (University of Waterloo, 2022-01-18)
      Explainable AI (XAI) methods are frequently applied to obtain qualitative insights about deep models' predictions. However, such insights need to be interpreted by a human observer to be useful. In this thesis, we aim to ...

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