Non-episodic Variational Discriminative Few-Shot Classifier

Few-shot learning techniques enable deep learning models to identify novel classes from one or only a few examples of it. This is achieved by using the subconcepts learnt from pre-training on base classes that share some of the characteristics of the novel classes. Most of the existing approaches use episodic training or episodic fine tuning (N-way K-shot) to learn these subconcepts. However, most studies in literature require different training models that are tied to the different N-way K-shot test settings; N represents the number of classes and K represents the number of samples in each class. Second, these models are trained for a large number of episodes and the memory requirements are also high for increasing values of N and K. In contrast, we propose a method that combines a \(\beta \) -variational autoencoder with cosine similarity classifier trained only once in an end-to-end (non-episodic) fashion on the complete training set. The end-to-end training enables the model to learn disentangled content and style representation. After training, the weights of novel classes are generated by normalizing the encoder’s content representation. Now the model can perform any N-way K-shot classification. Unlike the majority of results reported in literature, our work includes the results for the case where the test set includes both base and novel class images. We tested our model on the Omniglot dataset. Our model performed on par with the state-of-the-art methods. In a few settings it also outperformed the existing methods.