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 3001–3025 of 3687 papers

TitleStatusHype
Urban Representation Learning for Fine-grained Economic Mapping: A Semi-supervised Graph-based Approach—0
Selective Inference for Sparse Multitask Regression with Applications in Neuroimaging—0
Accent Conversion with Articulatory Representations—0
Self-boosted Time-series Forecasting with Multi-task and Multi-view Learning—0
All Birds with One Stone: Multi-task Text Classification for Efficient Inference with One Forward Pass—0
Self-Evolutionary Optimization for Pareto Front Learning—0
Self-Learning Camera: Autonomous Adaptation of Object Detectors to Unlabeled Video Streams—0
Self-MI: Efficient Multimodal Fusion via Self-Supervised Multi-Task Learning with Auxiliary Mutual Information Maximization—0
Self-Paced Multi-Task Learning—0
Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotations—0
All Birds with One Stone: Multi-task Learning for Inference with One Forward Pass—0
A Literature Review of Keyword Spotting Technologies for Urdu—0
Self-Supervised Embeddings for Detecting Individual Symptoms of Depression—0
XMusic: Towards a Generalized and Controllable Symbolic Music Generation Framework—0
Self-supervised Hypergraphs for Learning Multiple World Interpretations—0
Self-supervised Multimodal Speech Representations for the Assessment of Schizophrenia Symptoms—0
Self-supervised Multi-task Learning Framework for Safety and Health-Oriented Connected Driving Environment Perception using Onboard Camera—0
Self-Training and Multi-Task Learning for Limited Data: Evaluation Study on Object Detection—0
Semantic-CD: Remote Sensing Image Semantic Change Detection towards Open-vocabulary Setting—0
UserBERT: Self-supervised User Representation Learning—0
Aligned Multi-Task Gaussian Process—0
Semantic Multi-Resolution Communications—0
Semantics-Depth-Symbiosis: Deeply Coupled Semi-Supervised Learning of Semantics and Depth—0
Aligned Multi Objective Optimization—0
Semantic Structure Extraction for Spreadsheet Tables with a Multi-task Learning Architecture—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