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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 3451–3475 of 3687 papers

TitleStatusHype
Stock Index Prediction with Multi-task Learning and Word Polarity Over Time—0
A Framework for Unified Real-time Personalized and Non-Personalized Speech Enhancement—0
Enhancing Event Causality Identification with Rationale and Structure-Aware Causal Question Answering—0
Enhancing Fairness and Performance in Machine Learning Models: A Multi-Task Learning Approach with Monte-Carlo Dropout and Pareto Optimality—0
Enhancing Handwritten Text Recognition with N-gram sequence decomposition and Multitask Learning—0
Enhancing Knowledge Selection for Grounded Dialogues via Document Semantic Graphs—0
Cross-Lingual Text-to-Speech Using Multi-Task Learning and Speaker Classifier Joint Training—0
Enhancing Multi-task Learning Capability of Medical Generalist Foundation Model via Image-centric Multi-annotation Data—0
Cross-lingual Sentence Embedding using Multi-Task Learning—0
Enhancing Question Generation with Commonsense Knowledge—0
A Framework for Controllable Multi-objective Learning with Annealed Stein Variational Hypernetworks—0
Enhancing Romanian Offensive Language Detection through Knowledge Distillation, Multi-Task Learning, and Data Augmentation—0
Enhancing Self-Attention with Knowledge-Assisted Attention Maps—0
Enhancing Visual Forced Alignment with Local Context-Aware Feature Extraction and Multi-Task Learning—0
Affective Behaviour Analysis via Progressive Learning—0
Ensemble Manifold Segmentation for Model Distillation and Semi-supervised Learning—0
YONO: Modeling Multiple Heterogeneous Neural Networks on Microcontrollers—0
Environmental Noise Embeddings for Robust Speech Recognition—0
Video-Based Frame-Level Facial Analysis of Affective Behavior on Mobile Devices Using EfficientNets—0
Structural Supervision for Word Alignment and Machine Translation—0
Equitable Multi-task Learning—0
Cross Lingual Cross Corpus Speech Emotion Recognition—0
Structure-aware Fine-tuning for Code Pre-trained Models—0
ESALE: Enhancing Code-Summary Alignment Learning for Source Code Summarization—0
ESCL: Equivariant Self-Contrastive Learning for Sentence Representations—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