[3Rin4-58] Few-shot Learning with Data Augmentation with Generative Model.
キーワード：ワンショット学習、データ水増し、生成モデル
While deep learning, in general, requires a large amount of labeled data,
there are situations where only a few samples are available for some classes.
In theory, if we can predict the probabilistic distribution of the classes
based on the samples for other classes, we can leverage the distribution to train the model.
We augment the data for the class with few samples using the generative model trained on the other classes for a classification task. We applied this method on MNIST dataset and
evaluate it.
there are situations where only a few samples are available for some classes.
In theory, if we can predict the probabilistic distribution of the classes
based on the samples for other classes, we can leverage the distribution to train the model.
We augment the data for the class with few samples using the generative model trained on the other classes for a classification task. We applied this method on MNIST dataset and
evaluate it.
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