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/adrianjav/rotograd
공식 코드https://github.com/advanced-vision-and-learning-lab/hlast_deepship_parameterefficient
공식 코드Histogram-based Parameter-efficient Tuning for Passive Sonar Classification
https://github.com/aehrc/cxrmate
공식 코드Longitudinal Data and a Semantic Similarity Reward for Chest X-Ray Report Generation
https://github.com/aeitel/self_deepmask
공식 코드Self-supervised Transfer Learning for Instance Segmentation through Physical Interaction
https://github.com/agakshat/spacefortress
공식 코드https://github.com/agemagician/CodeTrans
공식 코드https://github.com/aggiejiang/crosslingual-offensive-language-survey
공식 코드https://github.com/aggiejiang/sexwes
공식 코드https://github.com/aghie/tree2labels
공식 코드Better, Faster, Stronger Sequence Tagging Constituent Parsers
https://github.com/agrija9/ssl-sonar-images
공식 코드https://github.com/aguerrerolopez/fa-vae
공식 코드Multimodal hierarchical Variational AutoEncoders with Factor Analysis latent space
https://github.com/aguirguis/collabml_client
공식 코드Accelerating Transfer Learning with Near-Data Computation on Cloud Object Stores
https://github.com/ahangchen/TFusion
공식 코드https://github.com/ahmadelsallab/MultiCheXNet
공식 코드https://github.com/ahmadnagib/SARL-RRM
공식 코드Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless Networks
https://github.com/ahmadnagib/tl-aided-drl
공식 코드https://github.com/ahmadtaheri2021/federated-deep-learning
공식 코드https://github.com/ahmadtaheri2021/lithology-microscopic-images-mini-dataset
공식 코드https://github.com/ahmed-khaled-saleh/fmtl-graph
공식 코드Learning to Collaborate Over Graphs: A Selective Federated Multi-Task Learning Approach
https://github.com/ahmed-mahran/mami2022
공식 코드Codec at SemEval-2022 Task 5: Multi-Modal Multi-Transformer Misogynous Meme Classification Framework
https://github.com/ahmetustun/fairseq
공식 코드When does Parameter-Efficient Transfer Learning Work for Machine Translation?
https://github.com/ahmetustun/hyperx
공식 코드Hyper-X: A Unified Hypernetwork for Multi-Task Multilingual Transfer
https://github.com/ahmetustun/udapter
공식 코드UDapter: Language Adaptation for Truly Universal Dependency Parsing
https://github.com/ai-4-atmosphere-remote-sensing/mt-hccar
공식 코드https://github.com/ai-agi/hicl
공식 코드https://github.com/ai-ku/Morse.jl
공식 코드https://github.com/ai-ku/TrMor2018
공식 코드https://github.com/ai-secure/secretgen
공식 코드SecretGen: Privacy Recovery on Pre-Trained Models via Distribution Discrimination
https://github.com/ai4co/awesome-fm4co
공식 코드MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts
https://github.com/ai4co/routefinder
공식 코드RouteFinder: Towards Foundation Models for Vehicle Routing Problems
https://github.com/aicps/rs2g
공식 코드https://github.com/aigeeksgroup/sss
공식 코드SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation
https://github.com/aignlai/holitrans
공식 코드Exploring Open-world Continual Learning with Knowns-Unknowns Knowledge Transfer
https://github.com/ailabudinegit/ontology-pretraining-code
공식 코드Generalizing over Long Tail Concepts for Medical Term Normalization
https://github.com/aim-skku/modeltinting
공식 코드Tint Your Models Task-wise for Improved Multi-task Model Merging
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.