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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/LiamMaclean216/Pytorch-Transfomer

Transformer Models for Text Coherence Assessment

https://github.com/Lightning-AI/lit-gpt

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/Lightning-AI/lit-llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/Lukeasargen/targetClassifier

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

https://github.com/MS-Mind/MS-Code-06/tree/main/PAMTRI

PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data

https://github.com/MS-P3/code4/tree/main/CIGF

Compressed Interaction Graph based Framework for Multi-behavior Recommendation

https://github.com/MS-P3/code4/tree/main/HPMR

Parallel Knowledge Enhancement based Framework for Multi-behavior Recommendation

https://github.com/MS-P3/code5/tree/main/llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/MS-P3/code5/tree/main/llama2

LLaMA: Open and Efficient Foundation Language Models

https://github.com/MS-P3/code7/tree/main/unispeech_sat

UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data

https://github.com/MS-P3/code7/tree/main/xlm_roberta_xl

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/MatGod7/Retinaface

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/MattWallingford/TAPS

Task Adaptive Parameter Sharing for Multi-Task Learning

https://github.com/Michaelgod/test

Two-Stream Convolutional Networks for Action Recognition in Videos

https://github.com/Mikoto10032/AutomaticWeightedLoss

Auxiliary Tasks in Multi-task Learning

https://github.com/Mind23-2/MindCode-131

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

https://github.com/Mind23-2/MindCode-138

Language Models are Few-Shot Learners

https://github.com/Mind23-2/MindCode-140

LLaMA: Open and Efficient Foundation Language Models

https://github.com/Mind23-2/MindCode-148

ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning

https://github.com/Mind23-2/MindCode-42

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

https://github.com/Mind23-2/MindCode-5/tree/main/PAMTRI

PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data

https://github.com/Mind23-2/MindCode-71

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/MindCode-4/code-1/tree/main/unispeech_sat

UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware Pre-Training

https://github.com/MindCode-4/code-11/tree/main/auxiliary-tasks-in-multi-task-learning

Auxiliary Tasks in Multi-task Learning

https://github.com/MindCode-4/code-3/tree/main/fsmt

Multi-Task Neural Models for Translating Between Styles Within and Across Languages

https://github.com/MindCode-4/code-5/tree/main/t5

ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning

https://github.com/MindCode-4/code-5/tree/main/unispeech

UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data

https://github.com/MindCode-4/code-5/tree/main/unispeech_sat

UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware Pre-Training

https://github.com/MindCode-4/code-6/tree/main/auxiliary-tasks-in-multi-task-learning

Auxiliary Tasks in Multi-task Learning

https://github.com/MindCode-4/code-8/tree/main/multi-task-learning-using-uncertainty-to

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

https://github.com/MindSpore-paper-code-2/code2/tree/main/PAMTRI

PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data

https://github.com/MindSpore-paper-code-3/code10/tree/main/fairmot

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

https://github.com/MindSpore-paper-code-3/code10/tree/main/faster_rcnn_ssod

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

https://github.com/MindSpore-paper-code-3/code3/tree/main/fairmot

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

https://github.com/MindSpore-paper-code-3/code3/tree/main/faster_rcnn_ssod

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

https://github.com/MindSpore-paper-code-3/code7/tree/main/fairmot

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

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.