Papers with Code 아카이브
Multi-Task Learning
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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/innovator-zero/fedhca2
FedHCA$^2$: Towards Hetero-Client Federated Multi-Task Learning
https://github.com/insait-institute/lm-evaluation-harness-bg
https://github.com/instructlab/instructlab
https://github.com/intellabs/fastrag
https://github.com/isl-org/multiobjectiveoptimization
https://github.com/ispamm/PRER
Bayesian Neural Networks With Maximum Mean Discrepancy Regularization
https://github.com/jackaduma/ChatGLM-LoRA-RLHF-PyTorch
https://github.com/jaryP/MMD-Bayesian-Neural-Network
Bayesian Neural Networks With Maximum Mean Discrepancy Regularization
https://github.com/jaryp/continual-learning-with-invertible-generative-models
Bayesian Neural Networks With Maximum Mean Discrepancy Regularization
https://github.com/jason718/game-feature-learning
Cross-Domain Self-supervised Multi-task Feature Learning using Synthetic Imagery
https://github.com/jason90330/EdgeFinal
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/jcwang123/ESD_Landmark_detection
https://github.com/jd-ai-research-silicon-valley/sae
Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents
https://github.com/jdfxzzy/dpmn
PEAN: A Diffusion-Based Prior-Enhanced Attention Network for Scene Text Image Super-Resolution
https://github.com/jerryljq/ActionRecognition
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/jestinas/covid
https://github.com/jetbrains-research/commit_message_generation
https://github.com/jh7cha/Towards-Real-Time-MOT
https://github.com/jhaprince/multibully
https://github.com/jiachengli1995/jointie
Generalizing Natural Language Analysis through Span-relation Representations
https://github.com/jiaruixu/st-cgan
https://github.com/jim-schwoebel/allie
https://github.com/jimheaton/Ultra96_ML_Embedded_Workshop
DenseBox: Unifying Landmark Localization with End to End Object Detection
https://github.com/jingcjie/FairMOT-mindspore-jupyter
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/jingyaogong/minimind
https://github.com/jmribeiro/UGP
Multi-task Learning and Catastrophic Forgetting in Continual Reinforcement Learning
https://github.com/jnulzl/Pytorch_Retinaface_To_Caffe
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/joannahong/lip-to-speech-synthesis-in-the-wild
Lip-to-Speech Synthesis in the Wild with Multi-task Learning
https://github.com/joelniklaus/lawinstruct
https://github.com/josselinsomervilleroberts/ptsl
BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning
https://github.com/jpablou/Matching-The-Blanks-Ths
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/juletx/lm-evaluation-harness
https://github.com/jung-jun-uk/mixface
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/junjieinkit/Towards-MOT
https://github.com/junqiangchen/COVID-CT19-Challenge
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.