https://github.com/jxhe/unify-parameter-efficient-tuning
공식 코드Towards a Unified View of Parameter-Efficient Transfer Learning
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/jy-stdio/mscmr_orient
공식 코드Orientation recognition and correction of Cardiac MRI with deep neural network
https://github.com/jyf123/html
공식 코드Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive Learning
https://github.com/jyzgh/fedlps
공식 코드FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing
https://github.com/jzbjyb/SpanRel
공식 코드Generalizing Natural Language Analysis through Span-relation Representations
https://github.com/jzhang538/badmerging
공식 코드https://github.com/k-han/DTC
공식 코드Learning to Discover Novel Visual Categories via Deep Transfer Clustering
https://github.com/kaai520/PGFA
공식 코드Zero-shot Skeleton-based Action Recognition with Prototype-guided Feature Alignment
https://github.com/kaczmarj/champkit
공식 코드https://github.com/kaeflint/GTI
공식 코드Gated Task Interaction Framework for Multi-task Sequence Tagging
https://github.com/kailiang-zhong/DESCN
공식 코드DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation
https://github.com/kaiqi123/cktf
공식 코드A Contrastive Knowledge Transfer Framework for Model Compression and Transfer Learning
https://github.com/kakaobrain/helo_word
공식 코드https://github.com/kaland313/Duckietown-RL
공식 코드Sim-to-real reinforcement learning applied to end-to-end vehicle control
https://github.com/kalrfou/swint-pretrained-microscopy-models
공식 코드https://github.com/kamalesh0406/Audio-Classification
공식 코드https://github.com/kamalojasv181/Hostility-Detection-in-Hindi-Posts
공식 코드Hostility Detection in Hindi leveraging Pre-Trained Language Models
https://github.com/kamperh/globalphone_awe
공식 코드Multilingual acoustic word embedding models for processing zero-resource languages
https://github.com/kaniblu/hanja-sisg
공식 코드Don't Just Scratch the Surface: Enhancing Word Representations for Korean with Hanja
https://github.com/kaopanboonyuen/reg
공식 코드https://github.com/katiana22/tl-deeponet
공식 코드Deep transfer operator learning for partial differential equations under conditional shift
https://github.com/kavehhassani/metagrl
공식 코드https://github.com/kawakera-lab/crossmodeltransfer
공식 코드Cross-Model Transfer of Task Vectors via Few-Shot Orthogonal Alignment
https://github.com/kb-ding/ear
공식 코드A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning
https://github.com/kdexd/virtex
공식 코드Multiple Instance Captioning: Learning Representations from Histopathology Textbooks and Articles
https://github.com/kding1225/softcpt
공식 코드Prompt Tuning with Soft Context Sharing for Vision-Language Models
https://github.com/kecsap/imagenet-clean
공식 코드Automated Cleanup of the ImageNet Dataset by Model Consensus, Explainability and Confident Learning
https://github.com/keeganhk/pointmcd
공식 코드https://github.com/keeplearning-again/matchseg
공식 코드MatchSeg: Towards Better Segmentation via Reference Image Matching
https://github.com/keiserlab/exceiver
공식 코드https://github.com/kenomo/ilid
공식 코드Industrial Language-Image Dataset (ILID): Adapting Vision Foundation Models for Industrial Settings
https://github.com/kenomo/industrial-clip
공식 코드Industrial Language-Image Dataset (ILID): Adapting Vision Foundation Models for Industrial Settings
https://github.com/kentonishi/jtr-cvpr-2024
공식 코드Joint-Task Regularization for Partially Labeled Multi-Task Learning
https://github.com/kenziyuliu/private-cross-silo-fl
공식 코드On Privacy and Personalization in Cross-Silo Federated Learning
https://github.com/keunwoochoi/transfer_learning_music
공식 코드Transfer learning for music classification and regression tasks
https://github.com/kexin-kxzhang/cd2-tsi
공식 코드Cross-Domain Conditional Diffusion Models for Time Series Imputation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.