Task-Driven Modular Networks for Zero-Shot Compositional Learning

Understand

One of the hallmarks of human intelligence is the ability to compose learned knowledge into novel concepts which can be recognized without a single training example.

• In contrast, current state-of-the-art methods require hundreds of training examples for each possible category to build reliable and accurate classifiers.
• To alleviate this striking difference in efficiency, we propose a task-driven modular architecture for compositional reasoning and sample efficient learning.
• Our architecture consists of a set of neural network modules, which are small fully connected layers operating in semantic concept space.