Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy
- Publisher:
- Institute of Electrical and Electronics Engineers
- Publication Type:
- Journal Article
- Citation:
- IEEE Transactions on Knowledge and Data Engineering, 2021, PP, (99), pp. 1-1
- Issue Date:
- 2021
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We study many-class few-shot (MCFS) problem in both supervised learning and
meta-learning settings. Compared to the well-studied many-class many-shot and
few-class few-shot problems, the MCFS problem commonly occurs in practical
applications but has been rarely studied in previous literature. It brings new
challenges of distinguishing between many classes given only a few training
samples per class. In this paper, we leverage the class hierarchy as a prior
knowledge to train a coarse-to-fine classifier that can produce accurate
predictions for MCFS problem in both settings. The propose model,
"memory-augmented hierarchical-classification network (MahiNet)", performs
coarse-to-fine classification where each coarse class can cover multiple fine
classes. Since it is challenging to directly distinguish a variety of fine
classes given few-shot data per class, MahiNet starts from learning a
classifier over coarse-classes with more training data whose labels are much
cheaper to obtain. The coarse classifier reduces the searching range over the
fine classes and thus alleviates the challenges from "many classes". On
architecture, MahiNet firstly deploys a convolutional neural network (CNN) to
extract features. It then integrates a memory-augmented attention module and a
multi-layer perceptron (MLP) together to produce the probabilities over coarse
and fine classes. While the MLP extends the linear classifier, the attention
module extends the KNN classifier, both together targeting the "few-shot"
problem. We design several training strategies of MahiNet for supervised
learning and meta-learning. In addition, we propose two novel benchmark
datasets "mcfsImageNet" and "mcfsOmniglot" specially designed for MCFS problem.
In experiments, we show that MahiNet outperforms several state-of-the-art
models on MCFS problems in both supervised learning and meta-learning.
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