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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

Showing 251300 of 3687 papers

TitleStatusHype
End-to-end Autonomous Driving with Semantic Depth Cloud Mapping and Multi-agentCode1
Detecting 32 Pedestrian Attributes for Autonomous VehiclesCode1
MetaBEV: Solving Sensor Failures for BEV Detection and Map SegmentationCode1
MetaICL: Learning to Learn In ContextCode1
Florence-2: Advancing a Unified Representation for a Variety of Vision TasksCode1
AV-RIR: Audio-Visual Room Impulse Response EstimationCode1
Forgetting to learn logic programsCode1
A Simple and Efficient Multi-Task Learning Approach for Conditioned Dialogue GenerationCode1
AutoMTL: A Programming Framework for Automating Efficient Multi-Task LearningCode1
FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear ModulationCode1
FINER: Enhancing State-of-the-art Classifiers with Feature Attribution to Facilitate Security AnalysisCode1
Aggression and Misogyny Detection using BERT: A Multi-Task ApproachCode1
BoIR: Box-Supervised Instance Representation for Multi-Person Pose EstimationCode1
Few-shot Knowledge Graph-to-Text Generation with Pretrained Language ModelsCode1
Fine-Tuning Attention Modules Only: Enhancing Weight Disentanglement in Task ArithmeticCode1
Generalized Multi-Task Learning from Substantially Unlabeled Multi-Source Medical Image DataCode1
Gradient Surgery for Multi-Task LearningCode1
FedCL: Federated Multi-Phase Curriculum Learning to Synchronously Correlate User HeterogeneityCode1
Extraction of cropland field parcels with high resolution remote sensing using multi-task learningCode1
Ditto: Fair and Robust Federated Learning Through PersonalizationCode1
Exploring Correlations of Self-Supervised Tasks for GraphsCode1
Exploiting Shared Representations for Personalized Federated LearningCode1
EXTENDING CONDITIONAL CONVOLUTION STRUCTURES FOR ENHANCING MULTITASKING CONTINUAL LEARNINGCode1
Federated Multi-Task Learning under a Mixture of DistributionsCode1
Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial TrainingCode1
Evaluating Logical Generalization in Graph Neural NetworksCode1
ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classificationCode1
ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft PromptsCode1
A Unified Span-Based Approach for Opinion Mining with Syntactic ConstituentsCode1
Exploiting Learnable Joint Groups for Hand Pose EstimationCode1
Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task LearningCode1
Audio-Visual Deception Detection: DOLOS Dataset and Parameter-Efficient Crossmodal LearningCode1
Evidentiality-guided Generation for Knowledge-Intensive NLP TasksCode1
Fair Resource Allocation in Multi-Task LearningCode1
A Unified Object Motion and Affinity Model for Online Multi-Object TrackingCode1
Feature Decomposition for Reducing Negative Transfer: A Novel Multi-task Learning Method for Recommender SystemCode1
A generic physics-informed neural network-based framework for reliability assessment of multi-state systemsCode1
End-to-end Emotion-Cause Pair Extraction via Learning to LinkCode1
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment AnalysisCode1
An Investigation of End-to-End Models for Robust Speech RecognitionCode1
Automatic Severity Classification of Dysarthric speech by using Self-supervised Model with Multi-task LearningCode1
Automatic Charge Identification from Facts: A Few Sentence-Level Charge Annotations is All You NeedCode1
A Gradually Soft Multi-Task and Data-Augmented Approach to Medical Question UnderstandingCode1
Annotating Columns with Pre-trained Language ModelsCode1
Encoding Visual Attributes in Capsules for Explainable Medical DiagnosesCode1
Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic EnvironmentsCode1
Flexible Style Image Super-Resolution using Conditional ObjectiveCode1
Anomaly Detection in Video via Self-Supervised and Multi-Task LearningCode1
End-to-End Multi-Task Learning with AttentionCode1
AdaMerging: Adaptive Model Merging for Multi-Task LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PCGrad∆m%125.7Unverified
2CAGrad∆m%112.8Unverified
3IMTL-G∆m%77.2Unverified
4Nash-MTL∆m%62Unverified
5BayesAgg-MTL∆m%53.7Unverified
#ModelMetricClaimedVerifiedStatus
1SwinMTLmIoU76.41Unverified
2Nash-MTLmIoU75.41Unverified
3MultiObjectiveOptimizationmIoU66.63Unverified
#ModelMetricClaimedVerifiedStatus
1SwinMTLMean IoU58.14Unverified
2Nash-MTLMean IoU40.13Unverified
#ModelMetricClaimedVerifiedStatus
1Gumbel-Matrix RoutingAverage Accuracy93.52Unverified
2Mixture-of-ExpertsAverage Accuracy92.19Unverified
#ModelMetricClaimedVerifiedStatus
1MGDA-UBError8.25Unverified
#ModelMetricClaimedVerifiedStatus
1BayesAgg-MTLdelta_m-2.23Unverified
#ModelMetricClaimedVerifiedStatus
1LETRFH83.3Unverified