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 1151–1175 of 3687 papers

TitleStatusHype
Improving Intrusion Detection with Domain-Invariant Representation Learning in Latent Space—0
Efficient Deweather Mixture-of-Experts with Uncertainty-aware Feature-wise Linear Modulation—0
Towards Invariant Soft Biometrics from Electrocardiograms—0
Solving Label Variation in Scientific Information Extraction via Multi-Task LearningCode0
FoodLMM: A Versatile Food Assistant using Large Multi-modal Model—0
Machine learning for structure-guided materials and process designCode0
BridgeNet: Comprehensive and Effective Feature Interactions via Bridge Feature for Multi-task Dense Predictions—0
Multi-task Learning To Improve Semantic Segmentation Of CBCT Scans Using Image Reconstruction—0
DSAF: A Dual-Stage Adaptive Framework for Numerical Weather Prediction DownscalingCode0
MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learning—0
Accelerating Meta-Learning by Sharing Gradients—0
Principled Weight Initialization for Hypernetworks—0
Order Matters in the Presence of Dataset Imbalance for Multilingual Learning—0
DFGET: Displacement-Field Assisted Graph Energy Transmitter for Gland Instance Segmentation—0
Dynamic Adjustment of Matching Radii under the Broadcasting Mode: A Novel Multitask Learning Strategy and Temporal Modeling Approach—0
Sim-to-Real Causal Transfer: A Metric Learning Approach to Causally-Aware Interaction RepresentationsCode0
GYM at Qur’an QA 2023 Shared Task: Multi-Task Transfer Learning for Quranic Passage Retrieval and Question Answering with Large Language ModelsCode0
Customizable Combination of Parameter-Efficient Modules for Multi-Task Learning—0
SequencePAR: Understanding Pedestrian Attributes via A Sequence Generation Paradigm—0
SEPSIS: I Can Catch Your Lies -- A New Paradigm for Deception Detection—0
Interpretable Meta-Learning of Physical Systems—0
ESM-NBR: fast and accurate nucleic acid-binding residue prediction via protein language model feature representation and multi-task learningCode0
Multi-task learning with cross-task consistency for improved depth estimation in colonoscopy—0
Syntax-Informed Interactive Model for Comprehensive Aspect-Based Sentiment Analysis—0
Task adaption by biologically inspired stochastic comodulation—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