Papers with Code 아카이브
Multi-Task Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 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/EleutherAI/lm-evaluation-harness
https://github.com/EleutherAI/lm_evaluation_harness
https://github.com/Faruk-Ahmed/detecting_semantic_anomalies
https://github.com/FlorentijnD/FairMOT
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction
https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction-roBERTa-5fold
https://github.com/George0828Zhang/simulst
CIF: Continuous Integrate-and-Fire for End-to-End Speech Recognition
https://github.com/GeorgeLuImmortal/Hierarchical-BERT-Model-with-Limited-Labelled-Data
https://github.com/GiorgosPanagopoulos/Influence-Maximization-via-Representation-Learning
Multi-task Learning for Influence Estimation and Maximization
https://github.com/GuoQuanhao/RetinaFace-Paddle
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/HazyResearch/metal
https://github.com/HoganZhang/FairMOT
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/Houssam93/MultiTask-Learning-NLP
https://github.com/HsinYingLee/OPN
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/Hui-Li/multi-task-learning-example-PyTorch
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
https://github.com/IBM/Dromedary
https://github.com/IMBINGO95/FairMOT
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/IQTLabs/Pytorch_Retinaface
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/IanChan711/ATM_ide
https://github.com/Ign0reLee/EMD_Neural_Style_Transfer_with_Tensorflow
A Unified Framework for Generalizable Style Transfer: Style and Content Separation
https://github.com/IliasPap/COVIDNet
https://github.com/IndicoDataSolutions/finetune
https://github.com/InfoX-SEU/DAE-AQA
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
https://github.com/InfoX-SEU/DAE_AQA
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
https://github.com/IsHYuhi/ST-CGAN_Stacked_Conditional_Generative_Adversarial_Networks
https://github.com/JakobHeiss/NN_regularization1
https://github.com/Johnny952/retinaface_mod
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/JonasFrey96/ml-hypersim-pytorch
Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding
https://github.com/JunweiLiang/Object_Detection_Tracking
https://github.com/JunweiLiang/social-distancing-prediction
Peeking into the Future: Predicting Future Person Activities and Locations in Videos
https://github.com/KarhouTam/FL-bench
https://github.com/Karthik-Bhaskar/Context-Based-Question-Answering
https://github.com/KnightZhang625/BERT_TF
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/LKLQQ/JDE
https://github.com/Leeroo-AI/mergoo
When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.