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Article overview
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Meta-Learning via Feature-Label Memory Network | | Dawit Mureja
; Hyunsin Park
; Chang D. Yoo
; | | Date: |
19 Oct 2017 | | Abstract: | Deep learning typically requires training a very capable architecture using
large datasets. However, many important learning problems demand an ability to
draw valid inferences from small size datasets, and such problems pose a
particular challenge for deep learning. In this regard, various researches on
"meta-learning" are being actively conducted. Recent work has suggested a
Memory Augmented Neural Network (MANN) for meta-learning. MANN is an
implementation of a Neural Turing Machine (NTM) with the ability to rapidly
assimilate new data in its memory, and use this data to make accurate
predictions. In models such as MANN, the input data samples and their
appropriate labels from previous step are bound together in the same memory
locations. This often leads to memory interference when performing a task as
these models have to retrieve a feature of an input from a certain memory
location and read only the label information bound to that location. In this
paper, we tried to address this issue by presenting a more robust MANN. We
revisited the idea of meta-learning and proposed a new memory augmented neural
network by explicitly splitting the external memory into feature and label
memories. The feature memory is used to store the features of input data
samples and the label memory stores their labels. Hence, when predicting the
label of a given input, our model uses its feature memory unit as a reference
to extract the stored feature of the input, and based on that feature, it
retrieves the label information of the input from the label memory unit. In
order for the network to function in this framework, a new memory-writingmodule
to encode label information into the label memory in accordance with the
meta-learning task structure is designed. Here, we demonstrate that our model
outperforms MANN by a large margin in supervised one-shot classification tasks
using Omniglot and MNIST datasets. | | Source: | arXiv, 1710.7110 | | Services: | Forum | Review | PDF | Favorites | |
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