Learning to Learn from Weak Supervision by Full Supervision

30 November 2017

Papers citing "Learning to Learn from Weak Supervision by Full Supervision"

6 / 6 papers shown

Title
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
Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning D. Hoang Cuong C. Nguyen Cuong Nguyen anh Belagiannis Vasileios G. Carneiro 25 2 0 17 Aug 2022
Making Every Label Count: Handling Semantic Imprecision by Integrating Domain Knowledge C. Brust Björn Barz Joachim Denzler NoLa 16 5 0 13 Oct 2020
Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods Daniel Y. Fu Mayee F. Chen Frederic Sala Sarah Hooper Kayvon Fatahalian Christopher Ré OffRL 23 111 0 27 Feb 2020
Multi-Resolution Weak Supervision for Sequential Data Frederic Sala P. Varma Jason Alan Fries Daniel Y. Fu Shiori Sagawa ... A. Ramamoorthy K. Xiao Kayvon Fatahalian J. Priest Christopher Ré NoLa 12 28 0 21 Oct 2019
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 338 11,684 0 09 Mar 2017