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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/MindSpore-paper-code-3/code7/tree/main/faster_rcnn_ssod

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

https://github.com/MindSpore-paper-code-3/code8/tree/main/fairmot

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

https://github.com/MindSpore-paper-code-3/code8/tree/main/faster_rcnn_ssod

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

https://github.com/MindSpore-paper-code-3/code9/tree/main/RetinaFace_ResNet50

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/MindSpore-paper-code-3/code9/tree/main/albert

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

https://github.com/MindSpore-scientific-2/code-1/tree/main/MTLN

Cross-stitch Networks for Multi-task Learning

https://github.com/MindSpore-scientific-2/code-10/tree/main/PCGrad-mindspore-example

A generic physics-informed neural network-based framework for reliability assessment of multi-state systems

https://github.com/MindSpore-scientific-2/code-14/tree/main/gpt_neox_japanese

GPT-NeoX-20B: An Open-Source Autoregressive Language Model

https://github.com/MindSpore-scientific-2/code-14/tree/main/llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/MindSpore-scientific-2/code-4/tree/main/PCGrad-mindspore-example

Gradient Surgery for Multi-Task Learning

https://github.com/MindSpore-scientific-2/code-5/tree/main/PCGrad-mindspore-example

Gradient Surgery for Multi-Task Learning

https://github.com/MindSpore-scientific-2/code-8/tree/main/PCGrad-mindspore-example

Gradient Surgery for Multi-Task Learning

https://github.com/MindSpore-scientific/code-1/tree/main/PCGrad-mindspore-example

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

https://github.com/MindSpore-scientific/code-10/tree/main/PCGrad-mindspore-example

Gradient Surgery for Multi-Task Learning

https://github.com/MindSpore-scientific/code-12/tree/main/MTLN

Cross-stitch Networks for Multi-task Learning

https://github.com/MindSpore-scientific/code-13/tree/main/PCGrad-mindspore-example

Gradient Surgery for Multi-Task Learning

https://github.com/MindSpore-scientific/code-4/tree/main/PCGrad-mindspore-example

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

https://github.com/MindSpore-scientific/code-6/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-scientific/code-8/tree/main/PCGrad-mindspore-example

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

https://github.com/MindSpore-scientific/code-9/tree/main/MTLN

Cross-stitch Networks for Multi-task Learning

https://github.com/MorningBooks/A_oveview_of_awesome_causality_dataset

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

https://github.com/MorningBooks/Causality

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

https://github.com/MyungsuChae/IROS2018_ws

End-to-end Multimodal Emotion and Gender Recognition with Dynamic Joint Loss Weights

https://github.com/NVIDIA/NeMo-Curator

Language Models are Few-Shot Learners

https://github.com/NathanDuran/Sentence-Encoding-for-DA-Classification

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/NeekHua/Pytorch_Retinaface_use_owner_data_test

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/OFRIN/Tensorflow_RetinaFace

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/OpenDriveLab/OpenLane

PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark

https://github.com/OrthoDex/PCGrad-PyTorch

Gradient Surgery for Multi-Task Learning

https://github.com/PINTO0309/PINTO_model_zoo/tree/main/326_YOLOPv2

YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception

https://github.com/PaddlePaddle/PaddleDetection

Towards Real-Time Multi-Object Tracking

https://github.com/PaddlePaddle/PaddleFL

LEAF: A Benchmark for Federated Settings

https://github.com/PaddlePaddle/PaddleNLP/blob/develop/paddlenlp/transformers/albert/modeling.py

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

https://github.com/PaddlePaddle/PaddleNLP/blob/develop/paddlenlp/transformers/roberta/modeling.py

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/gpt-3

Language Models are Few-Shot Learners

https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/ernie-1.0

ERNIE 2.0: A Continual Pre-training Framework for Language Understanding

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