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 2226–2250 of 3687 papers

TitleStatusHype
HyperGrid Transformers: Towards A Single Model for Multiple Tasks—0
UserBERT: Self-supervised User Representation Learning—0
Re-examining Routing Networks for Multi-task Learning—0
CANVASEMB: Learning Layout Representation with Large-scale Pre-training for Graphic Design—0
On the use of linguistic similarities to improve Neural Machine Translation for African Languages—0
Pseudo Label-Guided Multi Task Learning for Scene Understanding—0
Gated Ensemble of Spatio-temporal Mixture of Experts for Multi-task Learning in Ride-hailing System—0
Unified Mandarin TTS Front-end Based on Distilled BERT ModelCode1
Continual Learning in Task-Oriented Dialogue SystemsCode1
Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks—0
Enhancing Handwritten Text Recognition with N-gram sequence decomposition and Multitask Learning—0
A Paragraph-level Multi-task Learning Model for Scientific Fact-VerificationCode1
On Generating Extended Summaries of Long DocumentsCode1
A multi-task learning network using shared BERT models for aspect-based sentiment analysis—0
Spatio-temporal Multi-task Learning for Cardiac MRI Left Ventricle QuantificationCode0
Encoding Syntactic Knowledge in Transformer Encoder for Intent Detection and Slot Filling—0
Leveraging Meta-path Contexts for Classification in Heterogeneous Information Networks—0
Exploiting Learnable Joint Groups for Hand Pose EstimationCode1
MIX : a Multi-task Learning Approach to Solve Open-Domain Question Answering—0
Task Uncertainty Loss Reduce Negative Transfer in Asymmetric Multi-task Feature Learning—0
Real-time Multi-Task Diffractive Deep Neural Networks via Hardware-Software Co-design—0
Towards Recognizing New Semantic Concepts in New Visual DomainsCode1
LiteMuL: A Lightweight On-Device Sequence Tagger using Multi-task Learning—0
Invariant Feature Learning for Sensor-based Human Activity Recognition—0
Reasoning in Dialog: Improving Response Generation by Context Reading ComprehensionCode1
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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