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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/chandanrao007/https-github.com-Zhongdao-Towards-Realtime-MOT

Towards Real-Time Multi-Object Tracking

https://github.com/chaneyddtt/UDA-Animal-Pose

Learning from Synthetic Animals

https://github.com/chanind/frame-semantic-transformer

Open-Domain Frame Semantic Parsing Using Transformers

https://github.com/chaoyi-wu/finetune_llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/chaoyi-wu/pmc-llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/chen-si-jia/Trajectory-Long-tail-Distribution-for-MOT

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

https://github.com/chenllliang/Gradient-Vaccine

Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models

https://github.com/chrisociepa/allamo

PaLM: Scaling Language Modeling with Pathways

https://github.com/chunhuililili/mt_dnn

Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding

https://github.com/cle-ros/RoutingNetworks

Routing Networks and the Challenges of Modular and Compositional Computation

https://github.com/clovaai/textual-kd-slu

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/coastalcph/eu-politics-llms

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/coastalcph/zeroshot_lexglue

Scaling Instruction-Finetuned Language Models

https://github.com/code-implementation1/Code6/tree/main/PAMTRI

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

https://github.com/code-trip/insightface-mxnet

RetinaFace: Single-stage Dense Face Localisation in the Wild

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

Language Models are Few-Shot Learners

https://github.com/codefuse-ai/codefuse-evaluation

Evaluating Large Language Models Trained on Code

https://github.com/codegram/calbert

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

https://github.com/colintr/interactiveclustering

A Method for Discovering Novel Classes in Tabular Data

https://github.com/common-english/bert-all

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/conceptofmind/PaLM-flax

PaLM: Scaling Language Modeling with Pathways

https://github.com/consequentai/fneval

Mixtral of Experts

https://github.com/contextlab/abstract2paper

Language Models are Few-Shot Learners

https://github.com/cranial-xix/famo

Gradient Surgery for Multi-Task Learning

https://github.com/crazydigger/Callibration-of-GPT

Language Models are Few-Shot Learners

https://github.com/cui0523/Code6/tree/main/albert

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

https://github.com/cycloneboy/pdf_table

An End-to-End Multi-Task Learning Model for Image-based Table Recognition

https://github.com/cypressd1999/FYP_2021

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

https://github.com/damien911224/theWorldInSafety

Two-Stream Convolutional Networks for Action Recognition in Videos

https://github.com/danielsyahputra/HydraNet

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

https://github.com/darkAlert/retinafacert

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/datvuthanh/HybridNets

YOLOP: You Only Look Once for Panoptic Driving Perception

https://github.com/deargen/MT-ENet

Improving evidential deep learning via multi-task learning

https://github.com/declare-lab/flan-alpaca

Scaling Instruction-Finetuned Language Models

https://github.com/declare-lab/red-instruct

Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic

https://github.com/dengyang17/dengyang17.github.io

Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering

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