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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 31–40 of 100 papers

TitleStatusHype
Perception Reinforcement Using Auxiliary Learning Feature Fusion: A Modified Yolov8 for Head Detection—0
Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning—0
Semantic-aware Temporal Channel-wise Attention for Cardiac Function Assessment—0
Predictive auxiliary objectives in deep RL mimic learning in the brain—0
Asset Bundling for Wind Power Forecasting—0
Large Language Models for Compiler Optimization—0
Point-TTA: Test-Time Adaptation for Point Cloud Registration Using Multitask Meta-Auxiliary Learning—0
Rethinking Transfer and Auxiliary Learning for Improving Audio Captioning Transformer—0
Multi-task Collaborative Pre-training and Individual-adaptive-tokens Fine-tuning: A Unified Framework for Brain Representation Learning—0
Bootstrapped Representations in Reinforcement Learning—0
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