44,804 research outputs found
Labeled Memory Networks for Online Model Adaptation
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.Comment: Accepted at AAAI 2018, 8 page
Memory Aware Synapses: Learning what (not) to forget
Humans can learn in a continuous manner. Old rarely utilized knowledge can be
overwritten by new incoming information while important, frequently used
knowledge is prevented from being erased. In artificial learning systems,
lifelong learning so far has focused mainly on accumulating knowledge over
tasks and overcoming catastrophic forgetting. In this paper, we argue that,
given the limited model capacity and the unlimited new information to be
learned, knowledge has to be preserved or erased selectively. Inspired by
neuroplasticity, we propose a novel approach for lifelong learning, coined
Memory Aware Synapses (MAS). It computes the importance of the parameters of a
neural network in an unsupervised and online manner. Given a new sample which
is fed to the network, MAS accumulates an importance measure for each parameter
of the network, based on how sensitive the predicted output function is to a
change in this parameter. When learning a new task, changes to important
parameters can then be penalized, effectively preventing important knowledge
related to previous tasks from being overwritten. Further, we show an
interesting connection between a local version of our method and Hebb's
rule,which is a model for the learning process in the brain. We test our method
on a sequence of object recognition tasks and on the challenging problem of
learning an embedding for predicting triplets.
We show state-of-the-art performance and, for the first time, the ability to
adapt the importance of the parameters based on unlabeled data towards what the
network needs (not) to forget, which may vary depending on test conditions.Comment: ECCV 201
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