  Deep time series metric learning is challenging due to the difficult
trade-off between temporal invariance to nonlinear distortion and
discriminative power in identifying non-matching sequences. This paper proposes
a novel neural network-based approach for robust yet discriminative time series
classification and verification. This approach adapts a parameterized attention
model to time warping for greater and more adaptive temporal invariance. It is
robust against not only local but also large global distortions, so that even
matching pairs that do not satisfy the monotonicity, continuity, and boundary
conditions can still be successfully identified. Learning of this model is
further guided by dynamic time warping to impose temporal constraints for
stabilized training and higher discriminative power. It can learn to augment
the inter-class variation through warping, so that similar but different
classes can be effectively distinguished. We experimentally demonstrate the
superiority of the proposed approach over previous non-parametric and deep
models by combining it with a deep online signature verification framework,
after confirming its promising behavior in single-letter handwriting
classification on the Unipen dataset.
