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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

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Showing 1451–1500 of 3687 papers

TitleStatusHype
Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyond—0
Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study—0
Bayesian Model Adaptation for Crowd Counts—0
Distribution Conditional Denoising: A Flexible Discriminative Image Denoiser—0
Distributional encoding for Gaussian process regression with qualitative inputs—0
Bayesian Inference with Posterior Regularization and applications to Infinite Latent SVMs—0
Extracting Fractional Inspiratory Time from Electrocardiograms—0
Adversarial Attribute-Image Person Re-identification—0
A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework—0
7th ABAW Competition: Multi-Task Learning and Compound Expression Recognition—0
Empirical Evaluation of Multi-task Learning in Deep Neural Networks for Natural Language Processing—0
Distributed Stochastic Multi-Task Learning with Graph Regularization—0
Distributed Primal-Dual Optimization for Online Multi-Task Learning—0
Distributed Networked Multi-task Learning—0
Distributed Multi-Task Relationship Learning—0
Bayesian Context Aggregation for Neural Processes—0
A Multi-Task Learning & Generation Framework: Valence-Arousal, Action Units & Primary Expressions—0
Distributed Multi-Task Learning with Shared Representation—0
Distributed Multi-Task Learning for Stochastic Bandits with Context Distribution and Stage-wise Constraints—0
BASIC: Semi-supervised Multi-organ Segmentation with Balanced Subclass Regularization and Semantic-conflict Penalty—0
Distributed Multitask Learning—0
Distributed Deep Reinforcement Learning: An Overview—0
BanditMTL: Bandit-based Multi-task Learning for Text Classification—0
A Multi-Task Learning Framework for Sound Event Detection using High-level Acoustic Characteristics of Sounds—0
Adversarial Attacks on Hidden Tasks in Multi-Task Learning—0
Distill Knowledge in Multi-task Reinforcement Learning with Optimal-Transport Regularization—0
Distillation based Multi-task Learning: A Candidate Generation Model for Improving Reading Duration—0
Baidu Translate: Research and Products—0
Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors—0
Bag-of-Words Transfer: Non-Contextual Techniques for Multi-Task Learning—0
Distance Correlation Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies—0
Disjoint Multi-task Learning between Heterogeneous Human-centric Tasks—0
Bagging Supervised Autoencoder Classifier for Credit Scoring—0
Disentangling Representations through Multi-task Learning—0
Disentangled Representation for Age-Invariant Face Recognition: A Mutual Information Minimization Perspective—0
Backdoor Attacks against Image-to-Image Networks—0
A Multi-task Learning Framework for Product Ranking with BERT—0
Adversarial Analysis of Natural Language Inference Systems—0
Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning—0
Disentangled Interleaving Variational Encoding—0
Discriminatory Transfer—0
Discriminative Feature Learning with Foreground Attention for Person Re-Identification—0
A Weighted Predict-and-Optimize Framework for Power System Operation Considering Varying Impacts of Uncertainty—0
Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation—0
Discourse-Aware Emotion Cause Extraction in Conversations—0
A Multi-task Learning Framework for Drone State Identification and Trajectory Prediction—0
Direct Preference Optimization-Enhanced Multi-Guided Diffusion Model for Traffic Scenario Generation—0
Directly Optimizing IoU for Bounding Box Localization—0
A Vanilla Multi-Task Framework for Dense Visual Prediction Solution to 1st VCL Challenge -- Multi-Task Robustness Track—0
Dining on Details: LLM-Guided Expert Networks for Fine-Grained Food Recognition—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