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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 51–100 of 100 papers

TitleStatusHype
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
Boost Test-Time Performance with Closed-Loop Inference—0
Predictive and Contrastive: Dual-Auxiliary Learning for Recommendation—0
Improving CTC-based speech recognition via knowledge transferring from pre-trained language modelsCode0
On Exploring Pose Estimation as an Auxiliary Learning Task for Visible-Infrared Person Re-identificationCode0
Auxiliary Learning for Self-Supervised Video Representation via Similarity-based Knowledge DistillationCode0
Raw Nav-merge Seismic Data to Subsurface Properties with MLP based Multi-Modal Information Unscrambler—0
Novel Natural Language Summarization of Program Code via Leveraging Multiple Input Representations—0
Efficient Multi-Task Auxiliary Learning: Selecting Auxiliary Data by Feature SimilarityCode0
Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction—0
Boost-RS: Boosted Embeddings for Recommender Systems and its Application to Enzyme-Substrate Interaction PredictionCode0
DistillPose: Lightweight Camera Localization Using Auxiliary Learning—0
Deep auxiliary learning for visual localization using colorization task—0
Test-Time Fast Adaptation for Dynamic Scene Deblurring via Meta-Auxiliary Learning—0
Auxiliary learning induced graph convolutional networks—0
Meta Auxiliary Learning for Facial Action Unit Detection—0
Pareto Self-Supervised Training for Few-Shot Learning—0
Auxiliary Tasks and Exploration Enable ObjectNavCode0
Embracing the Disharmony in Medical Imaging: A Simple and Effective Framework for Domain Adaptation—0
Siamese Labels Auxiliary Learning—0
JigsawGAN: Auxiliary Learning for Solving Jigsaw Puzzles with Generative Adversarial Networks—0
Auxiliary Tasks and Exploration Enable ObjectGoal Navigation—0
Meta Auxiliary Labels with Constituent-based Transformer for Aspect-based Sentiment Analysis—0
Multimodal Topic-Enriched Auxiliary Learning for Depression Detection—0
Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks—0
Leveraging Visual Question Answering to Improve Text-to-Image Synthesis—0
Self-supervised pre-training and contrastive representation learning for multiple-choice video QA—0
Auxiliary Learning by Implicit DifferentiationCode0
Joint learning of interpretation and distillation—0
Unsupervised Temporal Video Segmentation as an Auxiliary Task for Predicting the Remaining Surgery Duration—0
Learning Object Placements For Relational Instructions by Hallucinating Scene RepresentationsCode0
Cross-Modal Image Fusion Theory Guided by Subjective Visual Attention—0
A Cross-Modal Image Fusion Method Guided by Human Visual Characteristics—0
To Balance or Not to Balance: A Simple-yet-Effective Approach for Learning with Long-Tailed Distributions—0
Learning to Generalize One Sample at a Time with Self-Supervision—0
Auxiliary Learning for Deep Multi-task Learning—0
SALT: Subspace Alignment as an Auxiliary Learning Task for Domain Adaptation—0
Hierarchical Auxiliary Learning—0
Dataset2Vec: Learning Dataset Meta-FeaturesCode0
Handling Noisy Labels for Robustly Learning from Self-Training Data for Low-Resource Sequence Labeling—0
NeurAll: Towards a Unified Visual Perception Model for Automated Driving—0
Self-Supervised Generalisation with Meta Auxiliary LearningCode0
Deep Auxiliary Learning for Visual Localization and OdometryCode0
Multimodal Classification for Analysing Social Media—0
A Refined End-to-End Discourse Parser—0
Leveraging Synthetic Discourse Data via Multi-task Learning for Implicit Discourse Relation Recognition—0
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