Papers with Code 아카이브

Multi-Task Learning

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하위 태스크: 2
평가표: 8
보존된 평가 기록: 16

Multi-task learning aims to learn multiple different tasks simultaneously while maximizing performance on one or all of the tasks.

<span style="color:grey; opacity: 0.6">( Image credit: Cross-stitch Networks for Multi-task Learning )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/NVlabs/PAMTRI

공식 코드

PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data

https://github.com/NeurAI-Lab/UniNet

공식 코드

UniNet: A Unified Scene Understanding Network and Exploring Multi-Task Relationships through the Lens of Adversarial Attacks

https://github.com/Nic5472K/AutoML2020_ICML_MTL2L

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MTL2L: A Context Aware Neural Optimiser

https://github.com/NoakLiu/MT2ST

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MT2ST: Adaptive Multi-Task to Single-Task Learning

https://github.com/OpenDriveLab/PersFormer_3DLane

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PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark

https://github.com/OptMN-Lab/SAMO

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SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation

https://github.com/PaddlePaddle/ERNIE

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ERNIE 2.0: A Continual Pre-training Framework for Language Understanding

https://github.com/PaddlePaddle/PaddleHelix

공식 코드

Docking-based Virtual Screening with Multi-Task Learning

https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/ernie-tiny

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ERNIE 3.0 Tiny: Frustratingly Simple Method to Improve Task-Agnostic Distillation Generalization

https://github.com/PaoloBonettiPolimi/NonLinCTFA

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Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance Analysis

https://github.com/ParitoshParmar/HalluciNet

공식 코드

HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN

https://github.com/ParitoshParmar/MTL-AQA

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What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment

https://github.com/Peihao-Xiang/MTCAE-DFER

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MTCAE-DFER: Multi-Task Cascaded Autoencoder for Dynamic Facial Expression Recognition

https://github.com/Pengxin-Guo/HGNN

공식 코드

Deep Multi-Task Augmented Feature Learning via Hierarchical Graph Neural Network

https://github.com/PhoebusSi/CTIR

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Learning Class-Transductive Intent Representations for Zero-shot Intent Detection

https://github.com/Pongpisit-Thanasutives/Physics-Informed-Neural-Networks-Multitask-Learning

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Adversarial Multi-task Learning Enhanced Physics-informed Neural Networks for Solving Partial Differential Equations

https://github.com/Qrange-group/CEM

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Causal Inference for Chatting Handoff

https://github.com/RUCAIBox/Few-Shot-KG2Text

공식 코드

Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Models

https://github.com/Ramprasad-Group/copolymer_informatics

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Copolymer Informatics with Multi-Task Deep Neural Networks

https://github.com/RyanWangZf/SurvTRACE

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SurvTRACE: Transformers for Survival Analysis with Competing Events

https://github.com/SJYuCNEL/Bregman-Correntropy-Conditional-Divergence

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Measuring the Discrepancy between Conditional Distributions: Methods, Properties and Applications

https://github.com/SStarCCat/HM4SR

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Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendation

https://github.com/STORMTROOPERRR/RSISCD

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A Late-Stage Bitemporal Feature Fusion Network for Semantic Change Detection

https://github.com/SaadAhmedJamal/IEEE_DFC2023

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Data Fusion for Multi-Task Learning of Building Extraction and Height Estimation

https://github.com/Sanyuan-Chen/RecAdam

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Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting

https://github.com/SciML/NeuralPDE.jl

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Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

https://github.com/SendongZhao/Multi-Task-Learning-for-MER-and-MEN

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A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization

https://github.com/SeungoneKim/SICK_Summarization

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Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization

https://github.com/ShampooWang/SpeechCLIP_plus

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SpeechCLIP+: Self-supervised multi-task representation learning for speech via CLIP and speech-image data

https://github.com/ShannonAI/glyce

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Glyce: Glyph-vectors for Chinese Character Representations

https://github.com/SharifBioinf/DeePathology

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DeePathology: Deep Multi-Task Learning for Inferring Molecular Pathology from Cancer Transcriptome

https://github.com/Shen-Lab/SS-GCNs

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When Does Self-Supervision Help Graph Convolutional Networks?

https://github.com/ShengyuH/Scene-Recognition-in-3D

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Indoor Scene Recognition in 3D

https://github.com/ShuoZhangXJTU/PEDP

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"Think Before You Speak": Improving Multi-Action Dialog Policy by Planning Single-Action Dialogs

https://github.com/SijinKim/mepsnet

공식 코드

Restoring Spatially-Heterogeneous Distortions using Mixture of Experts Network

https://github.com/SimonVandenhende/Multi-Task-Learning-PyTorch

공식 코드

MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learning

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