  The success of deep learning models is heavily tied to the use of massive
amount of labeled data and excessively long training time. With the emergence
of intelligent edge applications that use these models, the critical challenge
is to obtain the same inference capability on a resource-constrained device
while providing adaptability to cope with the dynamic changes in the data. We
propose AgileNet, a novel lightweight dictionary-based few-shot learning
methodology which provides reduced complexity deep neural network for efficient
execution at the edge while enabling low-cost updates to capture the dynamics
of the new data. Evaluations of state-of-the-art few-shot learning benchmarks
demonstrate the superior accuracy of AgileNet compared to prior arts.
Additionally, AgileNet is the first few-shot learning approach that prevents
model updates by eliminating the knowledge obtained from the primary training.
This property is ensured through the dictionaries learned by our novel
end-to-end structured decomposition, which also reduces the memory footprint
and computation complexity to match the edge device constraints.
