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/Soikonomou/bert_new

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

https://github.com/Soikonomou/bert_new_new

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

https://github.com/StanislasBertrand/RetinaFace-tf2

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/StanislasBertrand/retinaface4j

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/Superminionsfy/Personalized-Data-Generation-using-GAN

LEAF: A Benchmark for Federated Settings

https://github.com/Sypherd/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/THUDM/CodeGeeX

Evaluating Large Language Models Trained on Code

https://github.com/THUDM/GLM

GLM-130B: An Open Bilingual Pre-trained Model

https://github.com/Tencent/TurboTransformers

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/TinDang97/face_recognition

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/Torilen/TER-LCF-ATEPC-FR

A Multi-task Learning Model for Chinese-oriented Aspect Polarity Classification and Aspect Term Extraction

https://github.com/Traffic-X/Open-TransMind

UFO: Unified Feature Optimization

https://github.com/Trent-tangtao/embedding

Learning to Make Predictions on Graphs with Autoencoders

https://github.com/TsingZ0/PFL-Non-IID

Federated Multi-Task Learning

https://github.com/UT-Austin-RPL/GIGA

Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations

https://github.com/UlionTse/mlgb

An Overview of Multi-Task Learning in Deep Neural Networks

https://github.com/UnknownGenie/altered-BERT-KPE

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/VachanVY/gpt.jax

Language Models are Few-Shot Learners

https://github.com/Vladimetr/ASR-Knowledge-Transferring

CIF: Continuous Integrate-and-Fire for End-to-End Speech Recognition

https://github.com/WangChen0902/RetinaFace-Paddle

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/WeiChengTseng/Pytorch-PCGrad

Gradient Surgery for Multi-Task Learning

https://github.com/WindChimeRan/OpenJERE

CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning

https://github.com/WuJie1010/Temporally-language-grounding

Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos

https://github.com/Xaniar87/SAN_SQuAD2

Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension

https://github.com/YaoXinZhi/BERT-for-BioNLP-OST2019-AGAC-Task2

A Frustratingly Easy Approach for Entity and Relation Extraction

https://github.com/ZHANG-SHI-CHANG/Pytorch_Retinaface

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/ZJUT-ERCISS/zjut_mindvideo/blob/master/msvideo/models/fairmot.py

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/ZexinYan/Medical-Report-Generation

On the Automatic Generation of Medical Imaging Reports

https://github.com/ZhangwenguangHikvision/YoloV5_JDE_TensorRT_for_Track

Towards Real-Time Multi-Object Tracking

https://github.com/ZongrenZou/MHPINN

L-HYDRA: Multi-Head Physics-Informed Neural Networks

https://github.com/aaaceo890/Attention

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

https://github.com/abdumaa/hiqualprop

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/abhaskumarsinha/Corpus2GPT

Language Models are Few-Shot Learners

https://github.com/abhaskumarsinha/MinimalGPT

Language Models are Few-Shot Learners

https://github.com/abhishekanand1710/noiseandbias

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/abrarum/bezierobjdet

Line Segment Detection Using Transformers without Edges

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