  Augmenting a neural network with memory that can grow without growing the
number of trained parameters is a recent powerful concept with many exciting
applications. We propose a design of memory augmented neural networks (MANNs)
called Labeled Memory Networks (LMNs) suited for tasks requiring online
adaptation in classification models. LMNs organize the memory with classes as
the primary key.The memory acts as a second boosted stage following a regular
neural network thereby allowing the memory and the primary network to play
complementary roles. Unlike existing MANNs that write to memory for every
instance and use LRU based memory replacement, LMNs write only for instances
with non-zero loss and use label-based memory replacement. We demonstrate
significant accuracy gains on various tasks including word-modelling and
few-shot learning. In this paper, we establish their potential in online
adapting a batch trained neural network to domain-relevant labeled data at
deployment time. We show that LMNs are better than other MANNs designed for
meta-learning. We also found them to be more accurate and faster than
state-of-the-art methods of retuning model parameters for adapting to
domain-specific labeled data.
