Papers with Code 아카이브
Transfer Learning
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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/rubensolv/InnateCoder
공식 코드InnateCoder: Learning Programmatic Options with Foundation Models
https://github.com/rucaibox/mpo
공식 코드Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models
https://github.com/rucaibox/mpoe
공식 코드Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models
https://github.com/ruchtem/moo-mtl
공식 코드https://github.com/rui-yan/ssl-fl
공식 코드https://github.com/ruidan/IMN-E2E-ABSA
공식 코드An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis
https://github.com/ruidaniel/rbaca
공식 코드Continual Deep Active Learning for Medical Imaging: Replay-Base Architecture for Context Adaptation
https://github.com/ruili33/sec
공식 코드Are Human-generated Demonstrations Necessary for In-context Learning?
https://github.com/ruipingl/skr_pea
공식 코드TransKD: Transformer Knowledge Distillation for Efficient Semantic Segmentation
https://github.com/ruipingl/transkd
공식 코드TransKD: Transformer Knowledge Distillation for Efficient Semantic Segmentation
https://github.com/ruishengsu/2.75d
공식 코드2.75D: Boosting learning by representing 3D Medical imaging to 2D features for small data
https://github.com/ruizhe-l/cmrxmotion
공식 코드https://github.com/ruoxi-jia-group/asset
공식 코드ASSET: Robust Backdoor Data Detection Across a Multiplicity of Deep Learning Paradigms
https://github.com/ruzhangzhao/htlgmm
공식 코드https://github.com/rwightman/pytorch-image-models
공식 코드https://github.com/rxzhen/mscmr-orient
공식 코드Cardiac MRI Orientation Recognition and Standardization using Deep Neural Networks
https://github.com/ryansteed/learning-appearance-bias
공식 코드A Set of Distinct Facial Traits Learned by Machines Is Not Predictive of Appearance Bias in the Wild
https://github.com/ryanwangzf/transtab
공식 코드TransTab: Learning Transferable Tabular Transformers Across Tables
https://github.com/ryanxingql/winner-ntire22-vqe
공식 코드Progressive Training of A Two-Stage Framework for Video Restoration
https://github.com/s-huu/pmtl
공식 코드Private Multi-Task Learning: Formulation and Applications to Federated Learning
https://github.com/s-nlp/formality
공식 코드Detecting Text Formality: A Study of Text Classification Approaches
https://github.com/s1998/progressivetraincodeswitch
공식 코드https://github.com/s3prl/s3prl
공식 코드TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech
https://github.com/s3prl/s3prl/tree/master/s3prl/upstream/distiller
공식 코드DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT
https://github.com/s4nyam/marioec
공식 코드https://github.com/s6fikass/Chatbot_KVNN
공식 코드Incorporating Joint Embeddings into Goal-Oriented Dialogues with Multi-Task Learning
https://github.com/saahiluppal/catr
공식 코드https://github.com/sabeesh90/Efficient-Deep-Learning-Casting-Defect-Detection-ICCR2021
공식 코드Efficient Deep Learning Methods for Identification of Defective Casting Products
https://github.com/sabeesh90/Layer_Fine_Tuning_dnn_CICT_2021
공식 코드Classification of Astronomical Bodies by Efficient Layer Fine-Tuning of Deep Neural Networks
https://github.com/sabeesh90/Multimodal_Deep_Learning_DLDC_2021
공식 코드https://github.com/sadransh/ontology-matching-framework
공식 코드https://github.com/safarisadegh/UL2DL
공식 코드Deep UL2DL: Channel Knowledge Transfer from Uplink to Downlink
https://github.com/sahelyiyi/FederatedLearning
공식 코드Clustered Federated Learning via Generalized Total Variation Minimization
https://github.com/sail-sg/mugs
공식 코드https://github.com/saili0103/TransCLIME
공식 코드Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Control
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.