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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 2126–2150 of 3687 papers

TitleStatusHype
Thompson Sampling with a Mixture Prior—0
Three Birds with One Stone: Multi-Task Temporal Action Detection via Recycling Temporal Annotations—0
Three Strategies to Improve One-to-Many Multilingual Translation—0
TIMIT Speaker Profiling: A Comparison of Multi-task learning and Single-task learning Approaches—0
TITAN: A Spatiotemporal Feature Learning Framework for Traffic Incident Duration Prediction—0
TMT: A Transformer-based Modal Translator for Improving Multimodal Sequence Representations in Audio Visual Scene-aware Dialog—0
To complete or to estimate, that is the question: A Multi-Task Approach to Depth Completion and Monocular Depth Estimation—0
Topic-Guided Abstractive Multi-Document Summarization—0
Topic Segmentation Model Focusing on Local Context—0
To Reverse the Gradient or Not: An Empirical Comparison of Adversarial and Multi-task Learning in Speech Recognition—0
Geometry-aware Two-scale PIFu Representation for Human Reconstruction—0
Towards Adversarially Robust Object Detection—0
Towards a Multi-modal, Multi-task Learning based Pre-training Framework for Document Representation Learning—0
Towards a One-stop Solution to Both Aspect Extraction and Sentiment Analysis Tasks with Neural Multi-task Learning—0
Towards a Real-Time Simulation of Elastoplastic Deformation Using Multi-Task Neural Networks—0
Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution—0
Towards automatic construction of multi-network models for heterogeneous multi-task learning—0
Towards Better Multi-task Learning: A Framework for Optimizing Dataset Combinations in Large Language Models—0
Reconsidering Learning Objectives in Unbiased Recommendation with Unobserved Confounders—0
Towards Consistent Multi-Task Learning: Unlocking the Potential of Task-Specific Parameters—0
Towards Emotion-aided Multi-modal Dialogue Act Classification—0
Towards Enhancing Faithfulness for Neural Machine Translation—0
Towards Generalized Models for Task-oriented Dialogue Modeling on Spoken Conversations—0
Towards Improving Abstractive Summarization via Entailment Generation—0
Towards Interpretable Multi-Task Learning Using Bilevel Programming—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