Papers with Code 아카이브

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/avivne/bilinear-transduction

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

https://github.com/awslabs/mlm-scoring

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/awsm-research/vulrepair

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

https://github.com/baichuan-inc/baichuan-13b

Measuring Massive Multitask Language Understanding

https://github.com/baichuan-inc/baichuan-7b

Measuring Massive Multitask Language Understanding

https://github.com/baichuan-inc/baichuan2

Measuring Massive Multitask Language Understanding

https://github.com/barisbatuhan/FaceDetector

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/batsresearch/alfred

LLaMA: Open and Efficient Foundation Language Models

https://github.com/bbruceyuan/ECPE-PyTorch

Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts

https://github.com/bcaitech1/p2-klue-Heeseok-Jeong

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/bcmi/SLBR-Visible-Watermark-Removal

Visible Watermark Removal via Self-calibrated Localization and Background Refinement

https://github.com/bdemo/pfedbred_public

Federated Multi-Task Learning under a Mixture of Distributions

https://github.com/beaupranisaa/EEGImageReconstruction

Multi-task Generative Adversarial Learning on Geometrical Shape Reconstruction from EEG Brain Signals

https://github.com/bellekci/Towards-Realtime-MOT-windows-py37

Towards Real-Time Multi-Object Tracking

https://github.com/benolmbrt/MedicOOD

Improving Calibration and Out-of-Distribution Detection in Medical Image Segmentation with Convolutional Neural Networks

https://github.com/benywon/Chinese-GPT-2

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

https://github.com/benywon/ReCO

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/beomi/koalpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/bfopengradient/NLP_ROBERTA

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/bigscience-workshop/Megatron-DeepSpeed

Language Models are Few-Shot Learners

https://github.com/bigscience-workshop/promptsource

Language Models are Few-Shot Learners

https://github.com/biubug6/Pytorch_Retinaface

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/black0017/MedicalZooPytorch

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

https://github.com/blawok/named-entity-recognition

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/bleakie/RetinaDetection

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/bleakie/RetinaDetector

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/bluejurand/Kaggle_QA_Google_Labeling

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/bramiozo/PubScience

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

https://github.com/brightmart/albert_zh

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

https://github.com/brightmart/roberta_zh

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/bubbliiiing/retinaface-keras

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/bunsenfeng/knowledge_card

Measuring Massive Multitask Language Understanding

https://github.com/byeongjun-park/SteerX

Switch Diffusion Transformer: Synergizing Denoising Tasks with Sparse Mixture-of-Experts

https://github.com/cardwing/Codes-for-Steering-Control

Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks

https://github.com/cciliber/matMTL

Convex Learning of Multiple Tasks and their Structure

https://github.com/cds-mipt/cds-tracking

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.