Leveraging Large Scale Models for Identifying and Fixing Deep Neural Networks Biases

In this breakout session the leaders will discuss the following questions and challenges with attendees: ● What are the examples of systematic approaches for using generative and multi-modal models to evaluate and improve robustness of deep neural networks? ● What are the limitations of using generative models in terms of sample quality and diversity? ● What are the challenges with using generative modeling based tools in special domains (e.g. medical) where available data is more limited? ● How do we monitor the biases of generative and multi-modal models themselves?