  We propose a kernelized deep local-patch descriptor based on efficient match
kernels of neural network activations. Response of each receptive field is
encoded together with its spatial location using explicit feature maps. Two
location parametrizations, Cartesian and polar, are used to provide robustness
to a different types of canonical patch misalignment. Additionally, we analyze
how the conventional architecture, i.e. a fully connected layer attached after
the convolutional part, encodes responses in a spatially variant way. In
contrary, explicit spatial encoding is used in our descriptor, whose potential
applications are not limited to local-patches. We evaluate the descriptor on
standard benchmarks. Both versions, encoding 32x32 or 64x64 patches,
consistently outperform all other methods on all benchmarks. The number of
parameters of the model is independent of the input patch resolution.
