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Auxiliary Learning

Auxiliary learning aims to find or design auxiliary tasks which can improve the performance on one or some primary tasks.

( Image credit: Self-Supervised Generalisation with Meta Auxiliary Learning )

Papers

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Showing 51–60 of 100 papers

TitleStatusHype
Pareto Self-Supervised Training for Few-Shot Learning—0
Perception Reinforcement Using Auxiliary Learning Feature Fusion: A Modified Yolov8 for Head Detection—0
Point-TTA: Test-Time Adaptation for Point Cloud Registration Using Multitask Meta-Auxiliary Learning—0
Predictive and Contrastive: Dual-Auxiliary Learning for Recommendation—0
Predictive auxiliary objectives in deep RL mimic learning in the brain—0
Raw Nav-merge Seismic Data to Subsurface Properties with MLP based Multi-Modal Information Unscrambler—0
Representation Learning For Efficient Deep Multi-Agent Reinforcement Learning—0
Rethinking Transfer and Auxiliary Learning for Improving Audio Captioning Transformer—0
SALT: Subspace Alignment as an Auxiliary Learning Task for Domain Adaptation—0
Self-supervised Auxiliary Learning for Texture and Model-based Hybrid Robust and Fair Featuring in Face Analysis—0
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