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Multi-Task Learning

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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/hamishivi/easylm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/hardianlawi/MTL-Homoscedastic-Uncertainty

Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

https://github.com/harsh2912/people-tracking

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

https://github.com/harshs27/neural-graph-revealers

uGLAD: Sparse graph recovery by optimizing deep unrolled networks

https://github.com/harshs27/tglad

uGLAD: Sparse graph recovery by optimizing deep unrolled networks

https://github.com/hav4ik/Hydra

Multi-Task Learning as Multi-Objective Optimization

https://github.com/hazyresearch/ama_prompting

Language Models are Few-Shot Learners

https://github.com/hbzhang/AwesomeSelfDriving

Reinforcement Learning and Deep Learning based Lateral Control for Autonomous Driving

https://github.com/helloyide/Cross-stitch-Networks-for-Multi-task-Learning

Cross-stitch Networks for Multi-task Learning

https://github.com/heshandevaka/Trade-Off-MOL

Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approach

https://github.com/hhj1897/face_detection

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/hieudepchai/BERT_IE

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

https://github.com/hilberthit/gpt-3

Language Models are Few-Shot Learners

https://github.com/hit-scir/chinese-mixtral-8x7b

Mixtral of Experts

https://github.com/hkuds/easyrec

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/hojjat-mokhtarabadi/promptsource

Language Models are Few-Shot Learners

https://github.com/hqhoangvuong/paperspace_ml_01

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/hsientzucheng/MKR.PyTorch

Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation

https://github.com/huangJC0429/TXtract

TXtract: Taxonomy-Aware Knowledge Extraction for Thousands of Product Categories

https://github.com/huangjiancong1/rlkit_autotune

Hyperparameter Auto-tuning in Self-Supervised Robotic Learning

https://github.com/huangshenneng/padd_retinaface

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/hugLiu/nlp-nsc

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

https://github.com/huggingface/hmtl

A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks

https://github.com/huggingface/transformers

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/hukkelas/DSFD-Pytorch-Inference

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/hustvl/YOLOP

YOLOP: You Only Look Once for Panoptic Driving Perception

https://github.com/hwwang55/KGCN

Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation

https://github.com/iChenning/facedetection

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/ibadr/RetinaFace

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/ibm/vira-intent-discovery

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/icalk-nlp/educhat

LLaMA: Open and Efficient Foundation Language Models

https://github.com/icey-zhang/RetinaFace_paddle

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/ictnlp/DSTC8-AVSD

Bridging Text and Video: A Universal Multimodal Transformer for Video-Audio Scene-Aware Dialog

https://github.com/idankinderman/fs_merge

Editing Models with Task Arithmetic

https://github.com/idiap/abroad-re

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/ililily/retinaface

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.