Theoretical Consistency

Uncertainty Quantification in CNN Through the Bootstrap of Convex Neural Networks

This paper proposes a novel bootstrap-based framework for uncertainty quantification (UQ) in Convolutional Neural Networks (CNNs), addressing the lack of theoretically consistent UQ tools. The method utilizes convexified neural networks to establish theoretical consistency, offers significantly less computational load, and explores a novel transfer learning approach.