Papers with Code 아카이브
Multi-Task Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/lixin4ever/BERT-E2E-ABSA
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis
https://github.com/lizonghang/leaf-mx
https://github.com/llamafamily/llama-chinese
https://github.com/lm-sys/routellm
https://github.com/lonePatient/albert_pytorch
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/longhao-chen/aicas2024
https://github.com/lonnyzhang423/github-hot-hub
You Only Learn One Representation: Unified Network for Multiple Tasks
https://github.com/ltruncel/Microsoft_Azure_50daysofudacity
https://github.com/lucidrains/CoCa-pytorch
https://github.com/lucidrains/PaLM-jax
https://github.com/lucidrains/PaLM-pytorch
https://github.com/lucidrains/compressive-transformer-pytorch
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/lucidrains/routing-transformer
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/lucidrains/sinkhorn-transformer
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/luciferbobo/dae-aqa
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
https://github.com/ludwig-ai/ludwig
https://github.com/lvyufeng/bert4ms
https://github.com/lyqcom/albert
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/lzzhaha/Multi-task-learning-for-Hate-Speech-Detection-Project
https://github.com/maftouni/Corona_CT_Classification
https://github.com/mahxn0/RetinaFace_For_Face_Plate_Detect
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/makefile/DCR
Revisiting RCNN: On Awakening the Classification Power of Faster RCNN
https://github.com/malin9402/arcface
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/mandiehyewon/TPAMTL
Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning
https://github.com/mandubian/codenets
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/markWJJ/Multitask-Learning
https://github.com/maudzung/TTNet-Real-time-Analysis-System-for-Table-Tennis-Pytorch
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
https://github.com/maudzung/TTNet-Realtime-for-Table-Tennis-Pytorch
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
https://github.com/may1114/triplet-loss
https://github.com/mazpie/mime
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
https://github.com/mbzuai-paris/lm-evaluation-harness-atlas-chat
https://github.com/mcgridles/LENS
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/med-air/Contrastive-COVIDNet
https://github.com/mediabrain-sjtu/medicalgpt-zh
https://github.com/meetyou-ai-lab/can-mc-evaluate-llms
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.