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 3001–3050 of 3687 papers

TitleStatusHype
Urban Representation Learning for Fine-grained Economic Mapping: A Semi-supervised Graph-based Approach—0
Selective Inference for Sparse Multitask Regression with Applications in Neuroimaging—0
Accent Conversion with Articulatory Representations—0
Self-boosted Time-series Forecasting with Multi-task and Multi-view Learning—0
All Birds with One Stone: Multi-task Text Classification for Efficient Inference with One Forward Pass—0
Self-Evolutionary Optimization for Pareto Front Learning—0
Self-Learning Camera: Autonomous Adaptation of Object Detectors to Unlabeled Video Streams—0
Self-MI: Efficient Multimodal Fusion via Self-Supervised Multi-Task Learning with Auxiliary Mutual Information Maximization—0
Self-Paced Multi-Task Learning—0
Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotations—0
All Birds with One Stone: Multi-task Learning for Inference with One Forward Pass—0
A Literature Review of Keyword Spotting Technologies for Urdu—0
Self-Supervised Embeddings for Detecting Individual Symptoms of Depression—0
XMusic: Towards a Generalized and Controllable Symbolic Music Generation Framework—0
Self-supervised Hypergraphs for Learning Multiple World Interpretations—0
Self-supervised Multimodal Speech Representations for the Assessment of Schizophrenia Symptoms—0
Self-supervised Multi-task Learning Framework for Safety and Health-Oriented Connected Driving Environment Perception using Onboard Camera—0
Self-Training and Multi-Task Learning for Limited Data: Evaluation Study on Object Detection—0
Semantic-CD: Remote Sensing Image Semantic Change Detection towards Open-vocabulary Setting—0
UserBERT: Self-supervised User Representation Learning—0
Aligned Multi-Task Gaussian Process—0
Semantic Multi-Resolution Communications—0
Semantics-Depth-Symbiosis: Deeply Coupled Semi-Supervised Learning of Semantics and Depth—0
Aligned Multi Objective Optimization—0
Semantic Structure Extraction for Spreadsheet Tables with a Multi-task Learning Architecture—0
Aligned Image-Word Representations Improve Inductive Transfer Across Vision-Language Tasks—0
Semi-Automatic RECIST Labeling on CT Scans with Cascaded Convolutional Neural Networks—0
Semisoft Task Clustering for Multi-Task Learning—0
User-Sensitive Recommendation Ensemble with Clustered Multi-Task Learning—0
Semi-Supervised Learning for Neural Keyphrase Generation—0
Semi-supervised Learning with a Teacher-student Network for Generalized Attribute Prediction—0
Using deep learning to predict soil properties from regional spectral data—0
Semi-Supervised Multi-Task Learning Based Framework for Power System Security Assessment—0
Semi-supervised Multi-task Learning for Semantics and Depth—0
Semi-supervised multi-task learning for lung cancer diagnosis—0
Using Eye-tracking Data to Predict the Readability of Brazilian Portuguese Sentences in Single-task, Multi-task and Sequential Transfer Learning Approaches—0
Semi-Supervised Multi-Task Learning With Chest X-Ray Images—0
Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study—0
Using Gaussian Processes for Rumour Stance Classification in Social Media—0
Sentence-Level BERT and Multi-Task Learning of Age and Gender in Social Media—0
Using Gaze to Predict Text Readability—0
Sentiment and Emotion help Sarcasm? A Multi-task Learning Framework for Multi-Modal Sarcasm, Sentiment and Emotion Analysis—0
Sentiment and Sarcasm Classification with Multitask Learning—0
Sentiment Domain Adaptation with Multiple Sources—0
Sentiment Expression Boundaries in Sentiment Polarity Classification—0
Using Multi-task and Transfer Learning to Solve Working Memory Tasks—0
SEPSIS: I Can Catch Your Lies -- A New Paradigm for Deception Detection—0
Seq2Seq and Multi-Task Learning for joint intent and content extraction for domain specific interpreters—0
SEQ+MD: Learning Multi-Task as a SEQuence with Multi-Distribution Data—0
Sequence-level self-learning with multiple hypotheses—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