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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 1–25 of 100 papers

TitleStatusHype
Leveraging Auxiliary Tasks with Affinity Learning for Weakly Supervised Semantic SegmentationCode1
RL-I2IT: Image-to-Image Translation with Deep Reinforcement LearningCode1
Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-LearningCode1
Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous GraphsCode1
Auxiliary Learning as an Asymmetric Bargaining GameCode1
Auto-Lambda: Disentangling Dynamic Task RelationshipsCode1
GeoAuxNet: Towards Universal 3D Representation Learning for Multi-sensor Point CloudsCode1
MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation ModelsCode1
Counting with Adaptive Auxiliary LearningCode1
A Survey on Cross-Domain Sequential RecommendationCode1
Learning to Recover Spectral Reflectance from RGB ImagesCode1
DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning—0
Auxiliary learning induced graph convolutional networks—0
Auxiliary Tasks and Exploration Enable ObjectGoal Navigation—0
Cross-Modal Image Fusion Theory Guided by Subjective Visual Attention—0
A Cross-Modal Image Fusion Method Guided by Human Visual Characteristics—0
Embracing the Disharmony in Medical Imaging: A Simple and Effective Framework for Domain Adaptation—0
Auxiliary Learning for Named Entity Recognition with Multiple Auxiliary Biomedical Training Data—0
Boost Test-Time Performance with Closed-Loop Inference—0
Asset Bundling for Wind Power Forecasting—0
Bootstrapped Representations in Reinforcement Learning—0
Hierarchical Auxiliary Learning—0
Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning—0
Introducing Depth into Transformer-based 3D Object Detection—0
Auxiliary Learning as a step towards Artificial General Intelligence—0
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