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

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Showing 226–250 of 3687 papers

TitleStatusHype
Histogram of Oriented Gradients Meet Deep Learning: A Novel Multi-task Deep Network for Medical Image Semantic SegmentationCode1
Large Scale Holistic Video UnderstandingCode1
How emotional are you? Neural Architectures for Emotion Intensity Prediction in MicroblogsCode1
Context-Aware Answer Extraction in Question AnsweringCode1
Hyperparameter optimization in deep multi-target predictionCode1
Improvable Gap Balancing for Multi-Task LearningCode1
Improved Feature Distillation via Projector EnsembleCode1
Adversarial Multi-task Learning Enhanced Physics-informed Neural Networks for Solving Partial Differential EquationsCode1
Improving End-to-End Task-Oriented Dialog System with A Simple Auxiliary TaskCode1
Combinatorial Optimization Perspective based Framework for Multi-behavior RecommendationCode1
Acoustic Prompt Tuning: Empowering Large Language Models with Audition CapabilitiesCode1
Improving Neural Cross-Lingual Summarization via Employing Optimal Transport Distance for Knowledge DistillationCode1
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth EstimationCode1
In Defense of the Unitary Scalarization for Deep Multi-Task LearningCode1
Indoor Scene Recognition in 3DCode1
CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance ComputingCode1
Compressed Context Memory For Online Language Model InteractionCode1
AIONER: All-in-one scheme-based biomedical named entity recognition using deep learningCode1
Analyzing Multi-Task Learning for Abstractive Text SummarizationCode1
Jointformer: Single-Frame Lifting Transformer with Error Prediction and Refinement for 3D Human Pose EstimationCode1
CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming SequencesCode1
Hierarchical Bayesian Modelling for Knowledge Transfer Across Engineering Fleets via Multitask LearningCode1
KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed VideosCode1
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and GenerationCode1
Compressed Interaction Graph based Framework for Multi-behavior RecommendationCode1
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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