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Auxiliary Learning
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Auxiliary learning aims to find or design auxiliary tasks which can improve the performance on one or some primary tasks.
<span style="color:grey; opacity: 0.6">( Image credit: Self-Supervised Generalisation with Meta Auxiliary Learning )</span>
논문
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Behavior Importance-Aware Graph Neural Architecture Search for Cross-Domain Recommendation
DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning
SGTC: Semantic-Guided Triplet Co-training for Sparsely Annotated Semi-Supervised Medical Image Segmentation
Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions
Self-supervised Auxiliary Learning for Texture and Model-based Hybrid Robust and Fair Featuring in Face Analysis
Learning Representation for Multitask learning through Self Supervised Auxiliary learning
When does Self-Prediction help? Understanding Auxiliary Tasks in Reinforcement Learning
Representation Learning For Efficient Deep Multi-Agent Reinforcement Learning
Aux-NAS: Exploiting Auxiliary Labels with Negligibly Extra Inference Cost
Meta-Auxiliary Learning for Micro-Expression Recognition
HiMAL: A Multimodal Hierarchical Multi-task Auxiliary Learning framework for predicting and explaining Alzheimer disease progression
Learning Multiple Representations with Inconsistency-Guided Detail Regularization for Mask-Guided Matting
GeoAuxNet: Towards Universal 3D Representation Learning for Multi-sensor Point Clouds
Auxiliary Tasks Enhanced Dual-affinity Learning for Weakly Supervised Semantic Segmentation
Two-Stage Multi-task Self-Supervised Learning for Medical Image Segmentation
Enhancing Molecular Property Prediction with Auxiliary Learning and Task-Specific Adaptation
A Survey on Cross-Domain Sequential Recommendation
Mitigate Domain Shift by Primary-Auxiliary Objectives Association for Generalizing Person ReID
Perception Reinforcement Using Auxiliary Learning Feature Fusion: A Modified Yolov8 for Head Detection
Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning
Semantic-aware Temporal Channel-wise Attention for Cardiac Function Assessment
Predictive auxiliary objectives in deep RL mimic learning in the brain
Asset Bundling for Wind Power Forecasting
RL-I2IT: Image-to-Image Translation with Deep Reinforcement Learning
Large Language Models for Compiler Optimization
Point-TTA: Test-Time Adaptation for Point Cloud Registration Using Multitask Meta-Auxiliary Learning
Multi-task Collaborative Pre-training and Individual-adaptive-tokens Fine-tuning: A Unified Framework for Brain Representation Learning
Bootstrapped Representations in Reinforcement Learning
LitCall: Learning Implicit Topology for CNN-based Aortic Landmark Localization
Meta-Auxiliary Learning for Adaptive Human Pose Prediction
Learning to Recover Spectral Reflectance from RGB Images
MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models
Introducing Depth into Transformer-based 3D Object Detection
Unified Convergence Theory of Stochastic and Variance-Reduced Cubic Newton Methods
Enhancing Deep Knowledge Tracing with Auxiliary Tasks
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