Choose Your Neuron: Incorporating Domain Knowledge through Neuron-Importance

TL;DR

This paper introduces NIWT, a zero-shot learning method that leverages neuron importance in CNNs to map domain knowledge onto learned concepts, enabling effective classification of unseen classes and interpretability.

Contribution

The paper presents NIWT, a novel zero-shot learning approach that uses neuron importance to incorporate domain knowledge and improve classification of unseen classes.

Findings

NIWT outperforms previous zero-shot learning methods on CUBirds and AWA2 benchmarks.

The approach can utilize various semantic inputs like attributes and natural language captions.

NIWT provides visual and textual explanations for its predictions.

Abstract

Individual neurons in convolutional neural networks supervised for image-level classification tasks have been shown to implicitly learn semantically meaningful concepts ranging from simple textures and shapes to whole or partial objects - forming a "dictionary" of concepts acquired through the learning process. In this work we introduce a simple, efficient zero-shot learning approach based on this observation. Our approach, which we call Neuron Importance-AwareWeight Transfer (NIWT), learns to map domain knowledge about novel "unseen" classes onto this dictionary of learned concepts and then optimizes for network parameters that can effectively combine these concepts - essentially learning classifiers by discovering and composing learned semantic concepts in deep networks. Our approach shows improvements over previous approaches on the CUBirds and AWA2 generalized zero-shot learning benchmarks. We demonstrate our approach on a diverse set of semantic inputs as external domain knowledge including attributes and natural language captions. Moreover by learning inverse mappings, NIWT can provide visual and textual explanations for the predictions made by the newly learned classifiers and provide neuron names. Our code is available at https://github.com/ramprs/neuron-importance-zsl.