Evaluating the Impact of Loss Function Variation in Deep Learning for Classification

28 October 2022

Papers citing "Evaluating the Impact of Loss Function Variation in Deep Learning for Classification"

10 / 10 papers shown

Title
A Comparative Study of Deep Learning Loss Functions for Multi-Label Remote Sensing Image Classification Hichame Yessou Gencer Sumbul Begüm Demir 41 31 0 29 Sep 2020
On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression Jun Qi Jun Du Sabato Marco Siniscalchi Xiaoli Ma Chin-Hui Lee 43 226 0 12 Aug 2020
An Ensemble of Simple Convolutional Neural Network Models for MNIST Digit Recognition Sanghyeon An Min Jun Lee Sanglee Park H. Yang Jungmin So 54 79 0 12 Aug 2020
Classification vs regression in overparameterized regimes: Does the loss function matter? Vidya Muthukumar Adhyyan Narang Vignesh Subramanian M. Belkin Daniel J. Hsu A. Sahai 86 151 0 16 May 2020
Heartbeat Classification in Wearables Using Multi-layer Perceptron and Time-Frequency Joint Distribution of ECG Anup Das F. Catthoor S. Schaafsma 38 36 0 13 Aug 2019
ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data F. Diakogiannis F. Waldner P. Caccetta Chen Wu SSeg 117 1,327 0 01 Apr 2019
Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks Zhenhua Feng J. Kittler Muhammad Awais P. Huber Xiaojun Wu CVBM 56 399 0 17 Nov 2017
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 283 8,904 0 25 Aug 2017
Focal Loss for Dense Object Detection Nayeon Lee Priya Goyal Ross B. Girshick Kaiming He Piotr Dollár ObjD 124 2,997 0 07 Aug 2017
On Loss Functions for Deep Neural Networks in Classification Katarzyna Janocha Wojciech M. Czarnecki UQCV 72 551 0 18 Feb 2017