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 2726–2750 of 3687 papers

TitleStatusHype
PitRSDNet: Predicting Intra-operative Remaining Surgery Duration in Endoscopic Pituitary Surgery—0
UnifiedABSA: A Unified ABSA Framework Based on Multi-task Instruction Tuning—0
Unified Detection of Digital and Physical Face Attacks—0
Skill Reinforcement Learning and Planning for Open-World Long-Horizon Tasks—0
An Active Learning Framework for Efficient Robust Policy Search—0
Optimal Algorithms for Online Convex Optimization with Adversarial Constraints—0
Active Multi-Task Representation Learning—0
A multi-task U-net for segmentation with lazy labels—0
Learning to segment microscopy images with lazy labels—0
Unified Multi-Criteria Chinese Word Segmentation with BERT—0
Unified Multi-Domain Learning and Data Imputation using Adversarial Autoencoder—0
Point or Generate Dialogue State Tracker—0
Point Process Modelling of Rumour Dynamics in Social Media—0
Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers—0
Polarity and Intensity: the Two Aspects of Sentiment Analysis—0
A Multi-task Two-stream Spatiotemporal Convolutional Neural Network for Convective Storm Nowcasting—0
A Multi-Task Self-Normalizing 3D-CNN to Infer Tuberculosis Radiological Manifestations—0
A Multi-Task, Multi-Modal Approach for Predicting Categorical and Dimensional Emotions—0
Polymer Informatics with Multi-Task Learning—0
Poly-PC: A Polyhedral Network for Multiple Point Cloud Tasks at Once—0
PoolRank: Max/Min Pooling-based Ranking Loss for Listwise Learning & Ranking Balance—0
Portrait Interpretation and a Benchmark—0
Pose is all you need: The pose only group activity recognition system (POGARS)—0
Action-Affect Classification and Morphing using Multi-Task Representation Learning—0
Positive-incentive Noise—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