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 1276–1300 of 3687 papers

TitleStatusHype
Multi-Task Learning based Video Anomaly Detection with Attention—0
Multi-Task Learning for Joint Semantic Role and Proto-Role Labeling—0
Can Demographic Factors Improve Text Classification? Revisiting Demographic Adaptation in the Age of TransformersCode0
Prompt-based Connective Prediction Method for Fine-grained Implicit Discourse Relation RecognitionCode0
Multi-Task Meta Learning: learn how to adapt to unseen tasksCode0
Composite Learning for Robust and Effective Dense Predictions—0
Explanations from Large Language Models Make Small Reasoners Better—0
Instruction Tuning for Few-Shot Aspect-Based Sentiment AnalysisCode1
Understanding Impacts of Task Similarity on Backdoor Attack and Detection—0
Semantic Cross Attention for Few-shot LearningCode0
Optimizing Evaluation Metrics for Multi-Task Learning via the Alternating Direction Method of Multipliers—0
Entity Tracking via Effective Use of Multi-Task Learning Model and Mention-guided DecodingCode0
Task Compass: Scaling Multi-task Pre-training with Task PrefixCode1
Semantic Segmentation under Adverse Conditions: A Weather and Nighttime-aware Synthetic Data-based ApproachCode1
TriangleNet: Edge Prior Augmented Network for Semantic Segmentation through Cross-Task ConsistencyCode0
Visually Similar Products Retrieval for Shopsy—0
Improved Abdominal Multi-Organ Segmentation via 3D Boundary-Constrained Deep Neural NetworksCode0
Improving End-to-End Text Image Translation From the Auxiliary Text Translation TaskCode0
Multi-Task Dynamical SystemsCode0
Data-Efficiency with a Single GPU: An Exploration of Transfer Methods for Small Language Models—0
GRANITE: A Graph Neural Network Model for Basic Block Throughput EstimationCode1
UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentationCode0
A Unified Framework for Multi-intent Spoken Language Understanding with promptingCode0
Causal Inference for Chatting HandoffCode1
Depth Is All You Need for Monocular 3D Detection—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