  In this paper, we propose a deep invertible hybrid model which integrates
discriminative and generative learning at a latent space level for
semi-supervised few-shot classification. Various tasks for classifying new
species from image data can be modeled as a semi-supervised few-shot
classification, which assumes a labeled and unlabeled training examples and a
small support set of the target classes. Predicting target classes with a few
support examples per class makes the learning task difficult for existing
semi-supervised classification methods, including selftraining, which
iteratively estimates class labels of unlabeled training examples to learn a
classifier for the training classes. To exploit unlabeled training examples
effectively, we adopt as the objective function the composite likelihood, which
integrates discriminative and generative learning and suits better with deep
neural networks than the parameter coupling prior, the other popular integrated
learning approach. In our proposed model, the discriminative and generative
models are respectively Prototypical Networks, which have shown excellent
performance in various kinds of few-shot learning, and Normalizing Flow a deep
invertible model which returns the exact marginal likelihood unlike the other
three major methods, i.e., VAE, GAN, and autoregressive model. Our main
originality lies in our integration of these components at a latent space
level, which is effective in preventing overfitting. Experiments using
mini-ImageNet and VGG-Face datasets show that our method outperforms
selftraining based Prototypical Networks.
