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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 51–75 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
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