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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 2201–2250 of 3687 papers

TitleStatusHype
Seed Stocking Via Multi-Task Learning—0
DBTagger: Multi-Task Learning for Keyword Mapping in NLIDBs Using Bi-Directional Recurrent Neural NetworksCode0
Explain and Predict, and then Predict AgainCode1
Transfer Learning and Augmentation for Word Sense Disambiguation—0
Joint Prediction of Remaining Useful Life and Failure Type of Train Wheelsets: A Multi-task Learning Approach—0
Spatial Object Recommendation with Hints: When Spatial Granularity Matters—0
Attention-based multi-task learning for speech-enhancement and speaker-identification in multi-speaker dialogue scenarioCode0
Contextual Classification Using Self-Supervised Auxiliary Models for Deep Neural NetworksCode0
Line Segment Detection Using Transformers without EdgesCode1
PhoNLP: A joint multi-task learning model for Vietnamese part-of-speech tagging, named entity recognition and dependency parsingCode1
Weakly Supervised Multi-Object Tracking and Segmentation—0
Learning Multiple Pixelwise Tasks Based on Loss Scale BalancingCode0
Graph-BAS3Net: Boundary-Aware Semi-Supervised Segmentation Network With Bilateral Graph Convolution—0
Learning With Privileged Tasks—0
Task Switching Network for Multi-Task Learning—0
Disentangled Representation for Age-Invariant Face Recognition: A Mutual Information Minimization Perspective—0
Student Customized Knowledge Distillation: Bridging the Gap Between Student and Teacher—0
Unsupervised Task Clustering for Multi-Task Reinforcement LearningCode0
Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach—0
Variational Multi-Task Learning—0
Bayesian Context Aggregation for Neural Processes—0
Towards Impartial Multi-task LearningCode1
Reusing Preprocessing Data as Auxiliary Supervision in Conversational Analysis—0
Information Transfer in Multi-Task Learning—0
Intra-layer Neural Architecture Search—0
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