Learning from Long-Tailed Noisy Data with Sample Selection and Balanced Loss

20 November 2022

Papers citing "Learning from Long-Tailed Noisy Data with Sample Selection and Balanced Loss"

5 / 5 papers shown

Title
DAdEE: Unsupervised Domain Adaptation in Early Exit PLMs Divya J. Bajpai M. Hanawal 31 1 0 06 Oct 2024
Balanced Meta-Softmax for Long-Tailed Visual Recognition Jiawei Ren Cunjun Yu Shunan Sheng Xiao Ma Haiyu Zhao Shuai Yi Hongsheng Li 159 549 0 21 Jul 2020
Equalization Loss for Long-Tailed Object Recognition Jingru Tan Changbao Wang Buyu Li Quanquan Li Wanli Ouyang Changqing Yin Junjie Yan 237 455 0 11 Mar 2020
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 282 39,170 0 01 Sep 2014
SMOTE: Synthetic Minority Over-sampling Technique Nitesh V. Chawla Kevin W. Bowyer Lawrence Hall W. Kegelmeyer AI4TS 160 25,214 0 09 Jun 2011