SOTAVerified

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 1601–1625 of 3687 papers

TitleStatusHype
Anomalous Sound Detection Using a Binary Classification Model and Class Centroids—0
DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy—0
Joint autoencoders: a flexible meta-learning framework—0
Joint auto-encoders: a flexible multi-task learning framework—0
Joint enhancement of automatic chest X-ray diagnosis and radiological gaze prediction with multi-stage cooperative learning—0
Deep Nonlinear Hyperspectral Unmixing Using Multi-task Learning—0
A Hierarchical Context-aware Modeling Approach for Multi-aspect and Multi-granular Pronunciation Assessment—0
Joint Entity and Relation Extraction for Legal Documents with Legal Feature Enhancement—0
Joint Estimation of Multiple RF Impairments Using Deep Multi-Task Learning—0
Jointly Modeling Hierarchical and Horizontal Features for Relational Triple Extraction—0
A Heterogeneous Graph-Based Multi-Task Learning for Fault Event Diagnosis in Smart Grid—0
Joint Goal Segmentation and Goal Success Prediction on Multi-Domain Conversations—0
DeepPSL: End-to-end perception and reasoning—0
A Unified Perspective on Multi-Domain and Multi-Task Learning—0
Few-Shot and Zero-Shot Learning for Historical Text Normalization—0
deep-REMAP: Parameterization of Stellar Spectra Using Regularized Multi-Task Learning—0
FewFedWeight: Few-shot Federated Learning Framework across Multiple NLP Tasks—0
deepSA2018 at SemEval-2018 Task 1: Multi-task Learning of Different Label for Affect in Tweets—0
Comparing Encoder-Only and Encoder-Decoder Transformers for Relation Extraction from Biomedical Texts: An Empirical Study on Ten Benchmark Datasets—0
Joint Learning On The Hierarchy Representation for Fine-Grained Human Action Recognition—0
Jointly Detecting and Separating Singing Voice: A Multi-Task Approach—0
Jointly Identifying Rhetoric and Implicit Emotions via Multi-Task Learning—0
Jointly Learning Clinical Entities and Relations with Contextual Language Models and Explicit Context—0
Jointly learning heterogeneous features for rgb-d activity recognition—0
FedNILM: Applying Federated Learning to NILM Applications at the Edge—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