Search Results for author: Renaud Seguier

Found 6 papers, 0 papers with code

MES-Loss: Mutually equidistant separation metric learning loss function

no code implementations Pattern Recognition Letters 2023 Yasser Boutaleb, Catherine Soladie, Nam-Duong Duong, Amine Kacete, Jérôme Royan, Renaud Seguier

We propose in this paper a new composite DML loss function that, in addition to the intra-class compactness, explicitly implies regulations to enforce the best inter-class separation by mutually equidistantly distributing the centers of the classes.

Clustering Image Clustering +3

Emo-CNN for Perceiving Stress from Audio Signals: A Brain Chemistry Approach

no code implementations8 Jan 2020 Anup Anand Deshmukh, Catherine Soladie, Renaud Seguier

There are certain emotions which are given more importance due to their effectiveness in understanding human feelings.

feature selection

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