Disentangling Sampling and Labeling Bias for Learning in Large-Output
Spaces

Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces

12 May 2021

Wittawat Jitkrittum

Sadeep Jayasumana

Sashank J. Reddi

Sanjiv Kumar

Papers citing "Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces"

5 / 5 papers shown

Title
Graph Regularized Encoder Training for Extreme Classification Anshul Mittal Shikhar Mohan Deepak Saini Suchith C. Prabhu Jain jiao ... Manik Varma Sumeet Agarwal Soumen Chakrabarti Purushottam Kar Manik Varma GNN 41 0 0 28 Feb 2024
Towards Memory-Efficient Training for Extremely Large Output Spaces -- Learning with 500k Labels on a Single Commodity GPU Erik Schultheis Rohit Babbar 23 4 0 06 Jun 2023
NGAME: Negative Mining-aware Mini-batching for Extreme Classification Kunal Dahiya Nilesh Gupta Deepak Saini Akshay Soni Yajun Wang ... Sonu Mehta Ramachandran Ramjee Sumeet Agarwal Purushottam Kar Manik Varma 39 35 0 10 Jul 2022
A data-centric approach for improving ambiguous labels with combined semi-supervised classification and clustering Lars Schmarje M. Santarossa Simon-Martin Schroder Claudius Zelenka R. Kiko J. Stracke N. Volkmann Reinhard Koch 30 10 0 30 Jun 2021
Equalization Loss for Long-Tailed Object Recognition Jingru Tan Changbao Wang Buyu Li Quanquan Li Wanli Ouyang Changqing Yin Junjie Yan 269 457 0 11 Mar 2020