Papers with Code 아카이브
Multi-Task Learning
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- 평가표
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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/zytx121/geoground
공식 코드GeoGround: A Unified Large Vision-Language Model for Remote Sensing Visual Grounding
https://github.com/zywoosimple/rt-srts
공식 코드https://github.com/zzzhang-jx/docres
공식 코드DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks
https://gitlab.com/saviola/rocov2-code
공식 코드ROCOv2: Radiology Objects in COntext Version 2, an Updated Multimodal Image Dataset
https://gitlab.com/smontariol/multi-task-esg
공식 코드Multi-Task Learning for Features Extraction in Financial Annual Reports
https://gitlab.com/tarekiraki/the_smtl_framework
공식 코드Machine learning for structure-guided materials and process design
https://huggingface.co/facebook/muppet-roberta-base
공식 코드Muppet: Massive Multi-task Representations with Pre-Finetuning
https://huggingface.co/facebook/muppet-roberta-large
공식 코드Muppet: Massive Multi-task Representations with Pre-Finetuning
https://github.com/04RR/SAFENet-Pytorch-
SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature Extraction
https://github.com/10Exahertz/Text-ResNet-on-Sentimental-LIAR-Fake-News
https://github.com/15534081591/FairMOT
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/1996scarlet/Laser-Eye
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/1996scarlet/faster-mobile-retinaface
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/1hb6s7t/PCGrad-mindspore-example
https://github.com/2023-MindSpore-1/ms-code-13/tree/main/GPT
https://github.com/2023-MindSpore-1/ms-code-153
https://github.com/2023-MindSpore-1/ms-code-163
https://github.com/2023-MindSpore-1/ms-code-178
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/2023-MindSpore-1/ms-code-215/tree/main/PAMTRI
https://github.com/2023-MindSpore-1/ms-code-219/tree/main/albert
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/2023-MindSpore-1/ms-code-6/tree/main/centernet_resnet50_v1
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/2023-MindSpore-4/Code10/tree/main/RetinaFace_ResNet50
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/glm
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/glm3
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/llama
https://github.com/2023-MindSpore-4/Code13/tree/main/zhanglan/MLink-main
MLink: Linking Black-Box Models from Multiple Domains for Collaborative Inference
https://github.com/2023-MindSpore-4/Code13/tree/main/zhanglan/MLink-main/mlink_ms
MLink: Linking Black-Box Models from Multiple Domains for Collaborative Inference
https://github.com/2023-MindSpore-4/Code8/tree/main/centernet_resnet50_v1
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/2023-MindSpore-4/Code9/tree/main/RetinaFace_ResNet50
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/gpt_neox_japanese
https://github.com/2275519762/RetinaFace_Pytorch
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/2796gaurav/human-eval
https://github.com/8023looker/med-rr
https://github.com/ACA-Lab-SJTU/approximate-computing
AXNet: ApproXimate computing using an end-to-end trainable neural network
https://github.com/AIDefender/MyDiscor
DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.