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Multi-Task Learning

Multi-task learning aims to learn multiple different tasks simultaneously while maximizing performance on one or all of the tasks.

( Image credit: Cross-stitch Networks for Multi-task Learning )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1401–1425 of 3687 papers

TitleStatusHype
End-to-end Speech-to-Punctuated-Text Recognition—0
Knowing Earlier what Right Means to You: A Comprehensive VQA Dataset for Grounding Relative Directions via Multi-Task LearningCode0
Aspect-Based Sentiment Analysis using Local Context Focus Mechanism with DeBERTa—0
Re-weighting Negative Samples for Model-Agnostic Matching—0
Identification of Distorted RF Components via Deep Multi-Task LearningCode0
Adversarial Multi-Task Deep Learning for Noise-Robust Voice Activity Detection with Low Algorithmic DelayCode0
DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot LearningCode1
ABAW: Learning from Synthetic Data & Multi-Task Learning Challenges—0
Saliency-Regularized Deep Multi-Task LearningCode1
Digital-twin-enhanced metal tube bending forming real-time prediction method based on Multi-source-input MTLCode0
Speech Emotion: Investigating Model Representations, Multi-Task Learning and Knowledge Distillation—0
Asynchronous Convergence in Multi-Task Learning via Knowledge Distillation from Converged Tasks—0
Intent Discovery for Enterprise Virtual Assistants: Applications of Utterance Embedding and Clustering to Intent Mining—0
Zero-shot Entity Linking with Less DataCode3
MaChAmp at SemEval-2022 Tasks 2, 3, 4, 6, 10, 11, and 12: Multi-task Multi-lingual Learning for a Pre-selected Set of Semantic DatasetsCode0
taochen at SemEval-2022 Task 5: Multimodal Multitask Learning and Ensemble Learning—0
Enhancing Self-Attention with Knowledge-Assisted Attention Maps—0
Enhancing Knowledge Selection for Grounded Dialogues via Document Semantic Graphs—0
LITE: Intent-based Task Representation Learning Using Weak SupervisionCode0
PAIC at SemEval-2022 Task 5: Multi-Modal Misogynous Detection in MEMES with Multi-Task Learning And Multi-model Fusion—0
Detecting Moments of Change and Suicidal Risks in Longitudinal User Texts Using Multi-task LearningCode0
Multi-Task Learning to Capture Changes in Mood Over Time—0
Explaining Models of Mental Health via Clinically Grounded Auxiliary Tasks—0
Event Causality Identification via Generation of Important Context WordsCode0
An Understanding-Oriented Robust Machine Reading Comprehension ModelCode0
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All datasetsQM9Cityscapes testNYUv2OMNIGLOTCelebAUTKFacewireframe dataset

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PCGrad∆m%125.7—Unverified
2CAGrad∆m%112.8—Unverified
3IMTL-G∆m%77.2—Unverified
4Nash-MTL∆m%62—Unverified
5BayesAgg-MTL∆m%53.7—Unverified
#ModelMetricClaimedVerifiedStatus
1SwinMTLmIoU76.41—Unverified
2Nash-MTLmIoU75.41—Unverified
3MultiObjectiveOptimizationmIoU66.63—Unverified
#ModelMetricClaimedVerifiedStatus
1SwinMTLMean IoU58.14—Unverified
2Nash-MTLMean IoU40.13—Unverified
#ModelMetricClaimedVerifiedStatus
1Gumbel-Matrix RoutingAverage Accuracy93.52—Unverified
2Mixture-of-ExpertsAverage Accuracy92.19—Unverified
#ModelMetricClaimedVerifiedStatus
1MGDA-UBError8.25—Unverified
#ModelMetricClaimedVerifiedStatus
1BayesAgg-MTLdelta_m-2.23—Unverified
#ModelMetricClaimedVerifiedStatus
1LETRFH83.3—Unverified