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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
Entire Space Counterfactual Learning: Tuning, Analytical Properties and Industrial Applications—0
Fast Adaptation for Human Pose Estimation via Meta-Optimization—0
Federated Learning with Server Learning: Enhancing Performance for Non-IID Data—0
Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks—0
Handling Noisy Labels for Robustly Learning from Self-Training Data for Low-Resource Sequence Labeling—0
IDMS: Instance Depth for Multi-scale Monocular 3D Object Detection—0
Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction—0
JigsawGAN: Auxiliary Learning for Solving Jigsaw Puzzles with Generative Adversarial Networks—0
Joint learning of interpretation and distillation—0
LAL: Enhancing 3D Human Motion Prediction with Latency-aware Auxiliary Learning—0
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