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 3326–3350 of 3687 papers

TitleStatusHype
Joint Surgical Gesture and Task Classification with Multi-Task and Multimodal Learning—0
Evaluation of Feature-Space Speaker Adaptation for End-to-End Acoustic Models—0
Learning to Sketch with Shortcut Cycle Consistency—0
User-Sensitive Recommendation Ensemble with Clustered Multi-Task Learning—0
Strong Baselines for Neural Semi-supervised Learning under Domain ShiftCode0
Scheduled Multi-Task Learning: From Syntax to Translation—0
Taskonomy: Disentangling Task Transfer LearningCode0
Linguistically-Informed Self-Attention for Semantic Role LabelingCode0
Same Representation, Different Attentions: Shareable Sentence Representation Learning from Multiple Tasks—0
Multi-task Learning for Universal Sentence Embeddings: A Thorough Evaluation using Transfer and Auxiliary Tasks—0
Pix3D: Dataset and Methods for Single-Image 3D Shape ModelingCode0
Multi-Task Learning for Argumentation Mining in Low-Resource SettingsCode0
A Novel Multi-Task Tensor Correlation Neural Network for Facial Attribute Prediction—0
Linguistically-Informed Self-Attention for Semantic Role Labeling—0
Ensemble Manifold Segmentation for Model Distillation and Semi-supervised Learning—0
Jointly Detecting and Separating Singing Voice: A Multi-Task Approach—0
FPAN: Fine-grained and Progressive Attention Localization Network for Data Retrieval—0
Speaker-Invariant Training via Adversarial Learning—0
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task LearningCode0
Deep Cascade Multi-task Learning for Slot Filling in Online Shopping AssistantCode0
Unsupervised Domain Adaptation: A Multi-task Learning-based Method—0
VQA-E: Explaining, Elaborating, and Enhancing Your Answers for Visual Questions—0
Revisiting RCNN: On Awakening the Classification Power of Faster RCNNCode0
Adversarial Data Programming: Using GANs to Relax the Bottleneck of Curated Labeled DataCode0
Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation—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