Reviews: Neural Similarity Learning

The authors propose to learn a custom similarity metric for CNNs together with adaptive kernel shape. This is formulated via learning a matrix M that modulates the application of a set of kernels W to the input X via f(W, X) W' M X. Structural constraints can be imposed on M to simplify optimization and minimize the number of parameters, but in its most general form it is capable of completely modifying the behavior of W. Although at test time M can be integrated into the weights W via matrix multiplication, during learning it regularizes training via matrix factorization. In addition, a variant is proposed where M is predicted dynamically given the input to the layer via a dedicated subnetwork. A comprehensive ablation analysis is provided that demonstrates that basic version of the proposed approach performs marginally better than a standard CNN with a comparable number of parameters on CIFAR-10, but the dynamic variant outperforms it by 1%.