DeepAI AI Chat
Log In Sign Up

Dynamic Time Warp Convolutional Networks

by   Yaniv Shulman, et al.

Where dealing with temporal sequences it is fair to assume that the same kind of deformations that motivated the development of the Dynamic Time Warp algorithm could be relevant also in the calculation of the dot product ("convolution") in a 1-D convolution layer. In this work a method is proposed for aligning the convolution filter and the input where they are locally out of phase utilising an algorithm similar to the Dynamic Time Warp. The proposed method enables embedding a non-parametric warping of temporal sequences for increasing similarity directly in deep networks and can expand on the generalisation capabilities and the capacity of standard 1-D convolution layer where local sequential deformations are present in the input. Experimental results demonstrate the proposed method exceeds or matches the standard 1-D convolution layer in terms of the maximum accuracy achieved on a number of time series classification tasks. In addition the impact of different hyperparameters settings is investigated given different datasets and the results support the conclusions of previous work done in relation to the choice of DTW parameter values. The proposed layer can be freely integrated with other typical layers to compose deep artificial neural networks of an arbitrary architecture that are trained using standard stochastic gradient descent.


page 1

page 2

page 3

page 4


NeuralWarp: Time-Series Similarity with Warping Networks

Research on time-series similarity measures has emphasized the need for ...

Cost-Sensitive Convolution based Neural Networks for Imbalanced Time-Series Classification

Some deep convolutional neural networks were proposed for time-series cl...

Anchor-Based Spatial-Temporal Attention Convolutional Networks for Dynamic 3D Point Cloud Sequences

Recently, learning based methods for the robot perception from the image...

Exploiting Local Structures with the Kronecker Layer in Convolutional Networks

In this paper, we propose and study a technique to reduce the number of ...

Memory Bounded Deep Convolutional Networks

In this work, we investigate the use of sparsity-inducing regularizers d...

Volumetric Convolution: Automatic Representation Learning in Unit Ball

Convolution is an efficient technique to obtain abstract feature represe...