In this paper, we show that, in vector-to-vector regression utilizing deep neural networks (DNNs), a generalized loss of mean absolute error (MAE) between the predicted and expected feature vectors is upper bounded by the sum of an approximation error, an estimation error, and an optimization error. Leveraging upon error decomposition techniques in statistical learning theory and non-convex optimization theory, we derive upper bounds for each of the three aforementioned errors and impose necessary constraints on DNN models. Moreover, we assess our theoretical results through a set of image de-noising and speech enhancement experiments. Our proposed upper bounds of MAE for DNN based vector-to-vector regression are corroborated by the experimental results and the upper bounds are valid with and without the 'over-parametrization' technique.

Qi J., Du J., Siniscalchi S.M., Ma X., Lee C.-H. (2020). Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network-Based Vector-to-Vector Regression. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 68, 3411-3422 [10.1109/TSP.2020.2993164].

Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network-Based Vector-to-Vector Regression

Siniscalchi S. M.
Supervision
;
2020-05-01

Abstract

In this paper, we show that, in vector-to-vector regression utilizing deep neural networks (DNNs), a generalized loss of mean absolute error (MAE) between the predicted and expected feature vectors is upper bounded by the sum of an approximation error, an estimation error, and an optimization error. Leveraging upon error decomposition techniques in statistical learning theory and non-convex optimization theory, we derive upper bounds for each of the three aforementioned errors and impose necessary constraints on DNN models. Moreover, we assess our theoretical results through a set of image de-noising and speech enhancement experiments. Our proposed upper bounds of MAE for DNN based vector-to-vector regression are corroborated by the experimental results and the upper bounds are valid with and without the 'over-parametrization' technique.
mag-2020
Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni
Qi J., Du J., Siniscalchi S.M., Ma X., Lee C.-H. (2020). Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network-Based Vector-to-Vector Regression. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 68, 3411-3422 [10.1109/TSP.2020.2993164].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/639407
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