Contextual HyperNetworks for Novel Feature Adaptation

Understand

While deep learning has obtained state-of-the-art results in many applications, the adaptation of neural network architectures to incorporate new output features remains a challenge, as neural networks are commonly trained to produce a fixed output dimension.

• This issue is particularly severe in online learning settings, where new output features, such as items in a recommender system, are added continually with few or no associated observations.
• As such, methods for adapting neural networks to novel features which are both time and data-efficient are desired.
• To address this, we propose the Contextual HyperNetwork (CHN), an auxiliary model which generates parameters for extending the base model to a new feature, by utilizing both existing data as well as any observations and/or metadata associated with the new feature.