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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 3426–3450 of 3687 papers

TitleStatusHype
Cross-Task Knowledge Distillation in Multi-Task Recommendation—0
Cross-Task Attention Network: Improving Multi-Task Learning for Medical Imaging Applications—0
Encoding Syntactic Knowledge in Transformer Encoder for Intent Detection and Slot Filling—0
End-to-end Anchored Speech Recognition—0
End-to-End ASR for Code-switched Hindi-English Speech—0
End-to-end cell recognition by point annotation—0
End-to-End Diagnosis and Segmentation Learning from Cardiac Magnetic Resonance Imaging—0
Cross-Sentence N-ary Relation Extraction with Graph LSTMs—0
End-to-End Learning of Task-Oriented Dialogs—0
A Fused Elastic Net Logistic Regression Model for Multi-Task Binary Classification—0
Stochastic Variational Deep Kernel Learning—0
Cross-modal Multi-task Learning for Graphic Recognition of Caricature Face—0
Cross-modal Deep Metric Learning with Multi-task Regularization—0
End-to-End Speaker Height and age estimation using Attention Mechanism with LSTM-RNN—0
End-to-end Speech-to-Punctuated-Text Recognition—0
End-to-End Speech-Translation with Knowledge Distillation: FBK@IWSLT2020—0
Energy Weighted Learning Progress Guided Interleaved Multi-Task Learning—0
English Conversational Telephone Speech Recognition by Humans and Machines—0
English-to-Chinese Transliteration with Phonetic Auxiliary Task—0
Enhanced Facet Generation with LLM Editing—0
Enhanced Knowledge Selection for Grounded Dialogues via Document Semantic Graphs—0
Enhanced Radar Perception via Multi-Task Learning: Towards Refined Data for Sensor Fusion Applications—0
Cross-Modal ASR Post-Processing System for Error Correction and Utterance Rejection—0
Enhancing and Adversarial: Improve ASR with Speaker Labels—0
Enhancing Automated Essay Scoring Performance via Fine-tuning Pre-trained Language Models with Combination of Regression and Ranking—0
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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