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Multi-Task Learning

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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/redcaps-dataset/pytorch-retinaface

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/rehamessameltagoury/Urban-Self-Driving-Car

End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual Perception

https://github.com/replicate/cog_stanford_alpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/rezkaaufar/pytorch-cvt

Semi-Supervised Sequence Modeling with Cross-View Training

https://github.com/ricaelum42/Contextual-Twitter-Sarcasm-Detection

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/rijgersberg/geitje

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/rjlallana/YOLO

You Only Learn One Representation: Unified Network for Multiple Tasks

https://github.com/roberttwomey/machine-imagination-isea

Language Models are Few-Shot Learners

https://github.com/roberttwomey/machine-imagination-workshop

Language Models are Few-Shot Learners

https://github.com/rubythonode/joint-many-task-model

A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks

https://github.com/ruc-nlpir/flashrag

REPLUG: Retrieval-Augmented Black-Box Language Models

https://github.com/rvlopes/gloria

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

https://github.com/s-ankur/fairseq-gec

Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data

https://github.com/s3prl/End-to-end-ASR-Pytorch

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

https://github.com/sabapathyn/Master

COVID-CT-Dataset: A CT Scan Dataset about COVID-19

https://github.com/safooray/tensor_factorization_mtl

Deep Multi-task Representation Learning: A Tensor Factorisation Approach

https://github.com/sagjounkani/Dont-Stop-Pretraining-Use-Adapters-Instead

AdapterFusion: Non-Destructive Task Composition for Transfer Learning

https://github.com/sajjjadayobi/FaceLib

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/salesforce/coderl

CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

https://github.com/sambanova/generative_data_prep

MetaICL: Learning to Learn In Context

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

Language Models are Few-Shot Learners

https://github.com/sanjass/MultiTaskLearning

Measuring Massive Multitask Language Understanding

https://github.com/sanyuan-chen/unispeech

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

https://github.com/saurabh-kataria/PERL-samples

Perceptual Loss based Speech Denoising with an ensemble of Audio Pattern Recognition and Self-Supervised Models

https://github.com/sclbd/deepfakebench

UCF: Uncovering Common Features for Generalizable Deepfake Detection

https://github.com/scrayish/ML_NLP

Language Models are Few-Shot Learners

https://github.com/scribbler00/task-TCN

Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time-Series Forecast

https://github.com/scuShaker/jde

Towards Real-Time Multi-Object Tracking

https://github.com/sdadas/polish-roberta

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/sebajoe/thresh

Thresh: A Unified, Customizable and Deployable Platform for Fine-Grained Text Evaluation

https://github.com/senguptaumd/SfSNet

An All-In-One Convolutional Neural Network for Face Analysis

https://github.com/seominseok0429/vidoe_tracking-Reidentification-Action_recognition_Demo

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

https://github.com/serengil/deepface

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/serengil/retinaface

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/shekharRavi/Beyond-Task-Success-NAACL2019

Beyond task success: A closer look at jointly learning to see, ask, and GuessWhat

https://github.com/shenweichen/DeepCTR

An Overview of Multi-Task Learning in Deep Neural Networks

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