  Few-shot models aim at making predictions using a minimal number of labeled
examples from a given task. The main challenge in this area is the one-shot
setting where only one element represents each class. We propose HyperShot -
the fusion of kernels and hypernetwork paradigm. Compared to reference
approaches that apply a gradient-based adjustment of the parameters, our model
aims to switch the classification module parameters depending on the task's
embedding. In practice, we utilize a hypernetwork, which takes the aggregated
information from support data and returns the classifier's parameters
handcrafted for the considered problem. Moreover, we introduce the kernel-based
representation of the support examples delivered to hypernetwork to create the
parameters of the classification module. Consequently, we rely on relations
between embeddings of the support examples instead of direct feature values
provided by the backbone models. Thanks to this approach, our model can adapt
to highly different tasks.
