https://github.com/ElofssonLab/ProQ4
공식 코드Deep transfer learning in the assessment of the quality of protein models
Papers with Code 아카이브
Transfer Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 4
- 평가표
- 6
- 보존된 평가 기록
- 10
Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.
<span style="color:grey; opacity: 0.6">( Image credit: Subodh Malgonde )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/EmbodiedVision/dlgpd
공식 코드Planning from Images with Deep Latent Gaussian Process Dynamics
https://github.com/Emotional-Text-to-Speech/dl-for-emo-tts
공식 코드https://github.com/Engler93/Self-Supervised-Autogenous-Learning
공식 코드Contextual Classification Using Self-Supervised Auxiliary Models for Deep Neural Networks
https://github.com/EnnengYang/AdaMerging
공식 코드https://github.com/EriCongMa/E2E_TIT_With_MT
공식 코드Improving End-to-End Text Image Translation From the Auxiliary Text Translation Task
https://github.com/EricFH/salient_object_ranking_with_PPA
공식 코드https://github.com/EtreSerBe/PokeAE
공식 코드Generating Gameplay-Relevant Art Assets with Transfer Learning
https://github.com/ExplainableML/czsl
공식 코드Learning Graph Embeddings for Compositional Zero-shot Learning
https://github.com/Exploration-Lab/FineGrained-Emotion-Prediciton-Using-Definitions
공식 코드Fine-Grained Emotion Prediction by Modeling Emotion Definitions
https://github.com/F2-Song/PromptSLU
공식 코드A Unified Framework for Multi-intent Spoken Language Understanding with prompting
https://github.com/FLHonker/Awesome-Knowledge-Distillation
공식 코드https://github.com/FangyunWei/SLRT/tree/main/TwoStreamNetwork
공식 코드A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation
https://github.com/FarhadMaleki/LodgedNet
공식 코드https://github.com/FatemehCh97/LiDAR-3D-Building-Modeling
공식 코드https://github.com/FedML-AI/FedML/tree/master/fedml_experiments/distributed/fedgkt
공식 코드Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge
https://github.com/FedML-AI/SpreadGNN
공식 코드SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks
https://github.com/FederatedAI/FATE-LLM
공식 코드FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models
https://github.com/Francesco-Sovrano/Deep-Learning-Based-Multi-Label-Text-Classification-of-UNGA-Resolutions
공식 코드Deep Learning Based Multi-Label Text Classification of UNGA Resolutions
https://github.com/Francois-Aubet/EQuANt
공식 코드https://github.com/Frankluox/Pytorch-MetaDataset
공식 코드https://github.com/Fraunhofer-SCAI/spectral_CoSMA
공식 코드Transfer Learning using Spectral Convolutional Autoencoders on Semi-Regular Surface Meshes
https://github.com/FraunhoferMEVIS/MedicalMultitaskModeling
공식 코드Overcoming Data Scarcity in Biomedical Imaging with a Foundational Multi-Task Model
https://github.com/Fritz449/ProtoNER
공식 코드https://github.com/FuchenUSTC/AherNet
공식 코드https://github.com/FuzhenZhuang/Transfer-Learning-Toolkit
공식 코드https://github.com/GGLAB-KU/gecturk
공식 코드GECTurk: Grammatical Error Correction and Detection Dataset for Turkish
https://github.com/GKalliatakis/Human-Rights-Archive-CNNs
공식 코드https://github.com/GMago-LeWay/GECFramework
공식 코드Detection-Correction Structure via General Language Model for Grammatical Error Correction
https://github.com/GRAAL-Research/deepparse
공식 코드Leveraging Subword Embeddings for Multinational Address Parsing
https://github.com/GT-SALT/Adaptive-Compositional-Modules
공식 코드Continual Sequence Generation with Adaptive Compositional Modules
https://github.com/GabrieleLagani/HebbianPCA
공식 코드Training Convolutional Neural Networks With Hebbian Principal Component Analysis
https://github.com/GabrieleLozupone/AXIAL
공식 코드https://github.com/Gaoyiminggithub/Graphonomy
공식 코드Graphonomy: Universal Human Parsing via Graph Transfer Learning
https://github.com/Gaoyiminggithub/Graphonomy-Panoptic
공식 코드Graphonomy: Universal Image Parsing via Graph Reasoning and Transfer
https://github.com/GatorSense/PlantRootSeg
공식 코드Overcoming Small Minirhizotron Datasets Using Transfer Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.