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Attentive Recurrent Comparators
Pranav Shyam · Shubham Gupta · Ambedkar Dukkipati

Mon Aug 07 01:30 AM -- 05:00 AM (PDT) @ Gallery #128

Rapid learning requires flexible representations to quickly adopt to new evidence. We develop a novel class of models called Attentive Recurrent Comparators (ARCs) that form representations of objects by cycling through them and making observations. Using the representations extracted by ARCs, we develop a way of approximating a \textit{dynamic representation space} and use it for one-shot learning. In the task of one-shot classification on the Omniglot dataset, we achieve the state of the art performance with an error rate of 1.5\%. This represents the first super-human result achieved for this task with a generic model that uses only pixel information.

Author Information

Pranav Shyam (R. V. College of Engineering & Indian Institute of Science)
Shubham Gupta (Indian Institute of Science)
Ambedkar Dukkipati (Indian Institute of Science)

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