Аннотация
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 ünseen" 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
<a href="https://github.com/ramprs/neuron-importance-zsl.">this https URL</a>
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