  We introduce a new regularization method for Artificial Neural Networks
(ANNs) based on Kernel Flows (KFs). KFs were introduced as a method for kernel
selection in regression/kriging based on the minimization of the loss of
accuracy incurred by halving the number of interpolation points in random
batches of the dataset. Writing $f_\theta(x) = \big(f^{(n)}_{\theta_n}\circ
f^{(n-1)}_{\theta_{n-1}} \circ \dots \circ f^{(1)}_{\theta_1}\big)(x)$ for the
functional representation of compositional structure of the ANN, the inner
layers outputs $h^{(i)}(x) = \big(f^{(i)}_{\theta_i}\circ
f^{(i-1)}_{\theta_{i-1}} \circ \dots \circ f^{(1)}_{\theta_1}\big)(x)$ define a
hierarchy of feature maps and kernels $k^{(i)}(x,x')=\exp(- \gamma_i
\|h^{(i)}(x)-h^{(i)}(x')\|_2^2)$. When combined with a batch of the dataset
these kernels produce KF losses $e_2^{(i)}$ (the $L^2$ regression error
incurred by using a random half of the batch to predict the other half)
depending on parameters of inner layers $\theta_1,\ldots,\theta_i$ (and
$\gamma_i$). The proposed method simply consists in aggregating a subset of
these KF losses with a classical output loss. We test the proposed method on
CNNs and WRNs without alteration of structure nor output classifier and report
reduced test errors, decreased generalization gaps, and increased robustness to
distribution shift without significant increase in computational complexity. We
suspect that these results might be explained by the fact that while
conventional training only employs a linear functional (a generalized moment)
of the empirical distribution defined by the dataset and can be prone to
trapping in the Neural Tangent Kernel regime (under over-parameterizations),
the proposed loss function (defined as a nonlinear functional of the empirical
distribution) effectively trains the underlying kernel defined by the CNN
beyond regressing the data with that kernel.

Machine Learning;Machine Learning