Papers with Code 아카이브

Multi-Task Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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/s3prl/s3prl/tree/master/s3prl/upstream/distiller

공식 코드

DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT

https://github.com/s6fikass/Chatbot_KVNN

공식 코드

Incorporating Joint Embeddings into Goal-Oriented Dialogues with Multi-Task Learning

https://github.com/sahelyiyi/FederatedLearning

공식 코드

Clustered Federated Learning via Generalized Total Variation Minimization

https://github.com/salesforce/codet5

공식 코드

CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

https://github.com/salomonhotegni/mdmtn

공식 코드

Multi-Objective Optimization for Sparse Deep Multi-Task Learning

https://github.com/saltudelft/codefill

공식 코드

CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming Sequences

https://github.com/sami-fak/distributedmtlspca

공식 코드

Multi-task learning on the edge: cost-efficiency and theoretical optimality

https://github.com/samohtaerg/geoerm

공식 코드

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds

https://github.com/samra-irshad/3d-boundary-constrained-networks

공식 코드

Improved Abdominal Multi-Organ Segmentation via 3D Boundary-Constrained Deep Neural Networks

https://github.com/samsung/mt-llm-nlu

공식 코드

Large Language Models for Expansion of Spoken Language Understanding Systems to New Languages

https://github.com/sarashoouri/efficientmtl

공식 코드

Efficient Computation Sharing for Multi-Task Visual Scene Understanding

https://github.com/sashankmvv/Emotion-recognition

공식 코드

Facial Emotion Recognition: A multi-task approach using deep learning

https://github.com/scale-lab/adamtl

공식 코드

AdaMTL: Adaptive Input-dependent Inference for Efficient Multi-Task Learning

https://github.com/scale-lab/mtlora

공식 코드

MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-Task Learning

https://github.com/schroeterjulien/midl-2022-segmentation-consistent-lesion-counting

공식 코드

Segmentation-Consistent Probabilistic Lesion Counting

https://github.com/sebastianhafner/conturbancd

공식 코드

Continuous Urban Change Detection from Satellite Image Time Series with Temporal Feature Refinement and Multi-Task Integration

https://github.com/sebastianruder/sluice-networks

공식 코드

Latent Multi-task Architecture Learning

https://github.com/seblutz/JointFormer

공식 코드

Jointformer: Single-Frame Lifting Transformer with Error Prediction and Refinement for 3D Human Pose Estimation

https://github.com/selinakhan/stylistic-MTL-ukiyoe

공식 코드

Stylistic Multi-Task Analysis of Ukiyo-e Woodblock Prints

https://github.com/senyuanya/mmlc

공식 코드

Multi-task Meta Label Correction for Time Series Prediction

https://github.com/sessionrec/mtd

공식 코드

Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation

https://github.com/seungho-snu/fxsr

공식 코드

Flexible Style Image Super-Resolution using Conditional Objective

https://github.com/sflippl/multi-task

공식 코드

Inductive biases of multi-task learning and finetuning: multiple regimes of feature reuse

https://github.com/sfu-multimedia-lab/mlscic

공식 코드

Multi-Task Learning for Screen Content Image Coding

https://github.com/sh-src/autodp

공식 코드

Automated Multi-Task Learning for Joint Disease Prediction on Electronic Health Records

https://github.com/shaohua0116/MultiDigitMNIST

공식 코드

Regularizing Deep Multi-Task Networks using Orthogonal Gradients

https://github.com/sharc-lab/edge-moe

공식 코드

Edge-MoE: Memory-Efficient Multi-Task Vision Transformer Architecture with Task-level Sparsity via Mixture-of-Experts

https://github.com/sheffieldnlp/cwi

공식 코드

Strong Baselines for Complex Word Identification across Multiple Languages

https://github.com/shenglandu/pushboundary

공식 코드

Push-the-Boundary: Boundary-aware Feature Propagation for Semantic Segmentation of 3D Point Clouds

https://github.com/sheoyon-jhin/pad

공식 코드

Precursor-of-Anomaly Detection for Irregular Time Series

https://github.com/shiqiyu/opengait

공식 코드

Gait Patterns as Biomarkers: A Video-Based Approach for Classifying Scoliosis

https://github.com/shiv12spingo/PINN_Research/tree/main/Solidification_Problem

공식 코드

A Physics Informed Neural Network (PINN) Methodology for Coupled Moving Boundary PDEs

https://github.com/shivaat/MTLB-STRUCT

공식 코드

MTLB-STRUCT @PARSEME 2020: Capturing Unseen Multiword Expressions Using Multi-task Learning and Pre-trained Masked Language Models

https://github.com/shiyi-zh0408/nae_cvpr2024

공식 코드

Narrative Action Evaluation with Prompt-Guided Multimodal Interaction

https://github.com/shl5133/E2EECPE

공식 코드

End-to-end Emotion-Cause Pair Extraction via Learning to Link

https://github.com/shrezaei/MultitaskTrafficClassification

공식 코드

Multitask Learning for Network Traffic Classification

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.