Title:  Learning Representations for Missing Information in Neural Networks

Committee: 

Dr. AlRegib, Advisor    

Dr. Romberg, Chair

Dr. Davenport

Abstract:

The objective of the proposed research is to create novel representations for information that neural networks have not learned through training data. Recent studies reveal the vulnerability of deep neural networks against unexpected input which differs from training data. A factor that contributes to the vulnerability of deep networks is the limitation in activation-based representations. The activation-based representation is a representation obtained in the form of activation and constructed by weights that contain specific knowledge learned from training data. Since the activation-based representation is inherently focused on learned information, the unexpected input cannot be properly handled by relying on the activation-based representation. We propose to complement the activation-based representation by the information that could be obtained from the behavior of deep networks when they face unexpected inputs. In particular, we investigate gradient-based representations to characterize missing information that deep networks have not learned. The proposed research generalizes the geometric interpretation of gradients to practical scenarios of deep learning. In addition, we validate the effectiveness of the gradient-based representation in characterizing missing information for deep networks.