SOSELETO: A Unified Approach to Transfer Learning and Training with Noisy Labels

24 May 2018

Papers citing "SOSELETO: A Unified Approach to Transfer Learning and Training with Noisy Labels"

8 / 8 papers shown

Title
Handling Label Noise via Instance-Level Difficulty Modeling and Dynamic Optimization Kuan Zhang Chengliang Chai Jingzhe Xu Chi Zhang Ye Yuan Guoren Wang Lei Cao NoLa 66 0 0 01 May 2025
Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling Boshen Zhang Yuxi Li Yuanpeng Tu Jinlong Peng Yabiao Wang Cunlin Wu Yanghua Xiao Cairong Zhao NoLa 38 6 0 23 Aug 2022
Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels Evgenii Zheltonozhskii Chaim Baskin A. Mendelson A. Bronstein Or Litany SSL 33 92 0 25 Mar 2021
HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks Yuanyuan Chen Boyang Albert Li Han Yu Pengcheng Wu Chunyan Miao TDI 27 39 0 04 Feb 2021
Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond Risheng Liu Jiaxin Gao Jin Zhang Deyu Meng Zhouchen Lin AI4CE 67 223 0 27 Jan 2021
Label Noise Types and Their Effects on Deep Learning G. Algan ilkay Ulusoy NoLa 16 53 0 23 Mar 2020
Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey G. Algan ilkay Ulusoy NoLa VLM 27 323 0 11 Dec 2019
Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-tuning Weifeng Ge Yizhou Yu 94 233 0 28 Feb 2017