Papers with Code 아카이브

Multi-Task Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

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/PaddlePaddle/PaddleOCR

A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning

https://github.com/PaddlePaddle/PaddleRec

Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/dselect_k

DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning

https://github.com/PaddlePaddle/PaddleScience

Gradient Surgery for Multi-Task Learning

https://github.com/PaddlePaddle/PaddleSpeech

Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning

https://github.com/Param-Raval/ipro

Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal

https://github.com/Param-Raval/shadow-sight

Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal

https://github.com/ParitoshParmar/HalluciNet--PyTorch

HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN

https://github.com/PengZhenghao/AXNet

AXNet: ApproXimate computing using an end-to-end trainable neural network

https://github.com/Pillercottrer/radcap_project

On the Automatic Generation of Medical Imaging Reports

https://github.com/Pongpisit-Thanasutives/Multi-task-Physics-informed-neural-networks

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

https://github.com/QCOIN-LAB/E2ECPE

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

https://github.com/QCOIN-LAB/OTE-MTL

A Multi-task Learning Framework for Opinion Triplet Extraction

https://github.com/Qengineering/Face-Mask-Detection-Jetson-Nano

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/Qengineering/Face-Mask-Detection-Raspberry-Pi-64-bits

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/Qengineering/Face-detection-Raspberry-Pi-32-64-bits

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/RUCAIBox/LLMBox

Language Models are Few-Shot Learners

https://github.com/RamnathKumar181/Adversarial-Learning-Dynamics-in-RL

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

https://github.com/RashmiUSC/3D-RU-Net

3D RoI-aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation

https://github.com/RchalYang/Soft-Module

Multi-Task Reinforcement Learning with Soft Modularization

https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/RuisongZhou/RetinaFace.pytorch

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/SHI-Labs/Decoupled-Classification-Refinement

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

https://github.com/Samyu0304/thought-propagation

Language Models are Few-Shot Learners

https://github.com/SanaAwan5/FTLSimulation

Salvaging Federated Learning by Local Adaptation

https://github.com/Sean-Huang65/AUX-TS

Adaptive Transfer Learning on Graph Neural Networks

https://github.com/SharadGitHub/Self-Supervised-Autoencoder

Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

https://github.com/ShayekhBinIslam/openrag

Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks

https://github.com/ShreyasMalakarjunPatil/Neural-Sculpting

Neural Sculpting: Uncovering hierarchically modular task structure in neural networks through pruning and network analysis

https://github.com/Siddharthss500/zero-resource

Zero-Resource Cross-Domain Named Entity Recognition

https://github.com/SindhuMadi/FakeNewsDetection

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/SnailDev/github-hot-hub

You Only Learn One Representation: Unified Network for Multiple Tasks

https://github.com/SohamSarfare/ADS

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/Soikonomou/albert_final

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/Soikonomou/albert_final_infer12

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/Soikonomou/albert_final_infer8

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

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