Papers with Code 아카이브
Multi-Task Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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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/yangyucheng000/University/tree/main/model-3/roberta
https://github.com/yangyucheng000/University/tree/main/model-3/speech_to_text
https://github.com/yangyucheng000/VOGUE
https://github.com/yanyang19/ImPart
https://github.com/yaringal/multi-task-learning-example
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
https://github.com/yasarniyazoglu/YOLOR-----Object-Detection-in-Custom-Data-with-YoloR
You Only Learn One Representation: Unified Network for Multiple Tasks
https://github.com/yashraj02/RetinaFace
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/ycjing/Character-Stylization
https://github.com/ydhcg-bobo/stcmot
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/yishuwei2019/DL-based-dynamic-risk-prediciton
Deep Learning for Patient-Specific Kidney Graft Survival Analysis
https://github.com/yiwc/robotics-world
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
https://github.com/yixinyang69/newfedem
Federated Multi-Task Learning under a Mixture of Distributions
https://github.com/yli-z/ml4h_are_clinical_t5_models_better_for_clinical_text
https://github.com/ymcui/chinese-mixtral
https://github.com/yongbowin/san_mrc_annotation
Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension
https://github.com/youichiro/transformer-copy
https://github.com/young-geng/easylm
https://github.com/yuanmu97/secure-transformer-inference
https://github.com/yuantiku/fairseq-gec
https://github.com/yuzhimanhua/Multi-BioNER
Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning
https://github.com/yzyouzhang/AIR-ASVspoof
An Empirical Study on Channel Effects for Synthetic Voice Spoofing Countermeasure Systems
https://github.com/yzyouzhang/asvspoof2021_air
An Empirical Study on Channel Effects for Synthetic Voice Spoofing Countermeasure Systems
https://github.com/zach-zhiling-zheng/reticular_chemist
https://github.com/zaradana/Fast_BERT
https://github.com/zccyman/pose_estimation/tree/master/lib/models/pose_pcr.py
https://github.com/zchuz/timebench
https://github.com/zeakey/skeleton
https://github.com/zebangcheng/emotion-llama
MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning
https://github.com/zechenli03/sensorllm
https://github.com/zengwbz/Face-Tracking-usingFairMOT
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/zengyan-97/MultiT-C-Dialog
A Simple and Efficient Multi-Task Learning Approach for Conditioned Dialogue Generation
https://github.com/zfj1998/CodeBert-Code2Text
https://github.com/zhangyuwangumass/Glyph-based-Chinese-Character-Embedding
https://github.com/zhaoolee/garss
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
https://github.com/zhaoxin111/insightface
RetinaFace: Single-stage Dense Face Localisation in the Wild
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.