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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 26–50 of 100 papers

TitleStatusHype
Asset Bundling for Wind Power Forecasting—0
RL-I2IT: Image-to-Image Translation with Deep Reinforcement LearningCode1
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
LitCall: Learning Implicit Topology for CNN-based Aortic Landmark Localization—0
Meta-Auxiliary Learning for Adaptive Human Pose Prediction—0
Learning to Recover Spectral Reflectance from RGB ImagesCode1
MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation ModelsCode1
Introducing Depth into Transformer-based 3D Object Detection—0
Unified Convergence Theory of Stochastic and Variance-Reduced Cubic Newton Methods—0
Enhancing Deep Knowledge Tracing with Auxiliary TasksCode0
Auxiliary Learning as an Asymmetric Bargaining GameCode1
Benchmark for Uncertainty & Robustness in Self-Supervised LearningCode0
IDMS: Instance Depth for Multi-scale Monocular 3D Object Detection—0
Auxiliary Learning as a step towards Artificial General Intelligence—0
Entire Space Counterfactual Learning: Tuning, Analytical Properties and Industrial Applications—0
Federated Learning with Server Learning: Enhancing Performance for Non-IID Data—0
Test-time Adaptation for Real Image Denoising via Meta-transfer Learning—0
Meta Auxiliary Learning for Low-resource Spoken Language Understanding—0
Enhancing Sequential Recommendation with Graph Contrastive Learning—0
AANG: Automating Auxiliary LearningCode0
Auxiliary Learning for Named Entity Recognition with Multiple Auxiliary Biomedical Training Data—0
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