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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/national-center-for-ai-saudi-arabia/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/ncoop57/gpt-code-clippy

Language Models are Few-Shot Learners

https://github.com/nebuly-ai/nebullvm/tree/main/apps/accelerate/chatllama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/neelabh17/MAVI-Face

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/neil-zeng/asr

Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning

https://github.com/nemonameless/fairmot

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

https://github.com/nemonameless/jde

Towards Real-Time Multi-Object Tracking

https://github.com/neuralmagic/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/nguyenvulebinh/vietnamese-roberta

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/nhatduy19599/insightface-master

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/nii-yamagishilab/ClassNSeg

Multi-task Learning For Detecting and Segmenting Manipulated Facial Images and Videos

https://github.com/ninglab/ecellm

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/niuxiaozhang/mxnet-retinaface-to-gluon

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/nkluge-correa/teenytinyllama

Training Compute-Optimal Large Language Models

https://github.com/nlp-uoregon/mlmm-evaluation

Measuring Massive Multitask Language Understanding

https://github.com/nlp-uoregon/trankit

AdapterFusion: Non-Destructive Task Composition for Transfer Learning

https://github.com/nlx-group/overlapy

Language Models are Few-Shot Learners

https://github.com/noagarcia/context-art-classification

Context-Aware Embeddings for Automatic Art Analysis

https://github.com/nolanzzz/MTMCT

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

https://github.com/notmariekondo/notmariekondo

YOLOP: You Only Look Once for Panoptic Driving Perception

https://github.com/ntunlplab/traditional-chinese-alpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/nvlong21/person_Counting

Towards Real-Time Multi-Object Tracking

https://github.com/nzl-thu/musdl

What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment

https://github.com/obi-ml-public/ehr_deidentification

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/octanove/shiba

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/ofa-sys/expertllama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ohadrubin/rpt

LLaMA: Open and Efficient Foundation Language Models

https://github.com/oljikeboost/PlayerTracking

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

https://github.com/ollmer/mmlu

Measuring Massive Multitask Language Understanding

https://github.com/omarfoq/knn-per

Exploiting Shared Representations for Personalized Federated Learning

https://github.com/oneflow-inc/libai

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/open-finance-lab/finlora

BloombergGPT: A Large Language Model for Finance

https://github.com/openbiolink/promptsource

Language Models are Few-Shot Learners

https://github.com/opendilab/DI-engine

Multi-task Deep Reinforcement Learning with PopArt

https://github.com/opengptx/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/opengvlab/internimage

Uni-Perceiver v2: A Generalist Model for Large-Scale Vision and Vision-Language Tasks

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