Deep Transfer Learning for Multiple Class Novelty Detection

Understand

We propose a transfer learning-based solution for the problem of multiple class novelty detection.

• In particular, we propose an end-to-end deep-learning based approach in which we investigate how the knowledge contained in an external, out-of-distributional dataset can be used to improve the performance of a deep network for visual novelty detection.
• Our solution differs from the standard deep classification networks on two accounts.
• First, we use a novel loss function, membership loss, in addition to the classical cross-entropy loss for training networks.