Differentiable Metric

Differentiable metrics are functions that quantify properties of data or models in a way that allows for efficient optimization using gradient-based methods. Current research focuses on developing such metrics for diverse applications, including improving classifier performance, evaluating texture tileability, enhancing autonomous driving policies, and learning distances between complex data structures like graphs. This work is significant because it enables the application of powerful optimization techniques to problems previously intractable due to the non-differentiability of relevant performance measures, leading to improved algorithms and more effective models across various fields.