Understanding CNN Fragility When Learning With Imbalanced Data

17 October 2022

Papers citing "Understanding CNN Fragility When Learning With Imbalanced Data"

9 / 9 papers shown

Title
A margin-based replacement for cross-entropy loss Michael W. Spratling Heiko H. Schütt 68 0 0 21 Jan 2025
Conformal-in-the-Loop for Learning with Imbalanced Noisy Data J. B. Graham-Knight Jamil Fayyad Nourhan Bayasi Patricia Lasserre H. Najjaran 35 0 0 04 Nov 2024
Learning Confidence Bounds for Classification with Imbalanced Data Matt Clifford Jonathan Erskine Alexander Hepburn Raúl Santos-Rodríguez Dario Garcia-Garcia 30 0 0 16 Jul 2024
DT-DDNN: A Physical Layer Security Attack Detector in 5G RF Domain for CAVs Ghazal Asemian Mohammadreza Amini B. Kantarci Melike Erol-Kantarci 32 2 0 05 Mar 2024
Dual-path convolutional neural network using micro-FTIR imaging to predict breast cancer subtypes and biomarkers levels: estrogen receptor, progesterone receptor, HER2 and Ki67 Matheus del-Valle E. Bernardes D. Zezell 18 0 0 23 Oct 2023
Towards Understanding How Data Augmentation Works with Imbalanced Data Damien Dablain Nitesh V. Chawla AI4CE 26 2 0 12 Apr 2023
Interpretable ML for Imbalanced Data Damien Dablain C. Bellinger Bartosz Krawczyk D. Aha Nitesh V. Chawla 22 1 0 15 Dec 2022
BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition Boyan Zhou Quan Cui Xiu-Shen Wei Zhao-Min Chen 245 782 0 05 Dec 2019
SMOTE: Synthetic Minority Over-sampling Technique Nitesh V. Chawla Kevin W. Bowyer Lawrence Hall W. Kegelmeyer AI4TS 160 25,244 0 09 Jun 2011