OWAdapt: An adaptive loss function for deep learning using OWA operators

05/30/2023
by   Sebastián Maldonado, et al.
Universidad de los Andes
0

In this paper, we propose a fuzzy adaptive loss function for enhancing deep learning performance in classification tasks. Specifically, we redefine the cross-entropy loss to effectively address class-level noise conditions, including the challenging problem of class imbalance. Our approach introduces aggregation operators, leveraging the power of fuzzy logic to improve classification accuracy. The rationale behind our proposed method lies in the iterative up-weighting of class-level components within the loss function, focusing on those with larger errors. To achieve this, we employ the ordered weighted average (OWA) operator and combine it with an adaptive scheme for gradient-based learning. Through extensive experimentation, our method outperforms other commonly used loss functions, such as the standard cross-entropy or focal loss, across various binary and multiclass classification tasks. Furthermore, we explore the influence of hyperparameters associated with the OWA operators and present a default configuration that performs well across different experimental settings.

READ FULL TEXT

page 1

page 2

page 3

page 4

10/11/2018

Taming the Cross Entropy Loss

We present the Tamed Cross Entropy (TCE) loss function, a robust derivat...
06/29/2021

Fast and Accurate Road Crack Detection Based on Adaptive Cost-Sensitive Loss Function

Numerous detection problems in computer vision, including road crack det...
06/24/2022

Deformable CNN and Imbalance-Aware Feature Learning for Singing Technique Classification

Singing techniques are used for expressive vocal performances by employi...
07/13/2020

A Machine Learning Approach to Assess Student Group Collaboration Using Individual Level Behavioral Cues

K-12 classrooms consistently integrate collaboration as part of their le...
04/24/2019

A Novel Re-weighting Method for Connectionist Temporal Classification

The connectionist temporal classification (CTC) enables end-to-end seque...
11/18/2022

A Fair Loss Function for Network Pruning

Model pruning can enable the deployment of neural networks in environmen...
09/05/2023

BeeTLe: A Framework for Linear B-Cell Epitope Prediction and Classification

The process of identifying and characterizing B-cell epitopes, which are...

Please sign up or login with your details

Forgot password? Click here to reset