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/libreai/neural-painters-x
공식 코드https://github.com/lichuangnus/uno-dst
공식 코드UNO-DST: Leveraging Unlabelled Data in Zero-Shot Dialogue State Tracking
https://github.com/lifelonglab/tinysubnets
공식 코드TinySubNets: An efficient and low capacity continual learning strategy
https://github.com/lifu-tu/Study-NLP-Robustness
공식 코드An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models
https://github.com/lightonai/contrastive-embeddings-for-neural-architectures
공식 코드https://github.com/lijin118/3CATN
공식 코드https://github.com/lijin118/LPJT
공식 코드https://github.com/lijingzhu1/ett-ckge
공식 코드ETT-CKGE: Efficient Task-driven Tokens for Continual Knowledge Graph Embedding
https://github.com/liketheflower/simcrosstrans
공식 코드https://github.com/liketheflower/uoddm
공식 코드Unified Object Detector for Different Modalities based on Vision Transformers
https://github.com/likith012/muleeg
공식 코드https://github.com/lili-chen/SEER
공식 코드Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings
https://github.com/limbo0000/InstanceLoc
공식 코드Instance Localization for Self-supervised Detection Pretraining
https://github.com/limu-research/2025-sac-rnn-attention-kd
공식 코드Knowledge Distillation in RNN-Attention Models for Early Prediction of Student Performance
https://github.com/lin-tianwei/teamlora
공식 코드TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and Competition
https://github.com/linfunster/pp-til
공식 코드PP-TIL: Personalized Planning for Autonomous Driving with Instance-based Transfer Imitation Learning
https://github.com/lingochamp/multi-scale-bert-aes
공식 코드On the Use of BERT for Automated Essay Scoring: Joint Learning of Multi-Scale Essay Representation
https://github.com/linhuixiao/clip-vg
공식 코드CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual Grounding
https://github.com/linnanwang/AlphaX-NASBench101
공식 코드AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search
https://github.com/linziyi96/st-adapter
공식 코드ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning
https://github.com/linzwcs/see
공식 코드SEE: Continual Fine-tuning with Sequential Ensemble of Experts
https://github.com/lipingyi/fedssa
공식 코드https://github.com/liqiiiii/encapsulating-knowledge-in-one-prompt
공식 코드https://github.com/lishuhuai527/coco-unihuman
공식 코드https://github.com/litian96/ditto
공식 코드Ditto: Fair and Robust Federated Learning Through Personalization
https://github.com/liu-xb/DIM_GLO
공식 코드Domain Adaptive Person Re-Identification via Coupling Optimization
https://github.com/liu-yating/up-person
공식 코드UP-Person: Unified Parameter-Efficient Transfer Learning for Text-based Person Retrieval
https://github.com/liuboxun/LLM4CP
공식 코드LLM4CP: Adapting Large Language Models for Channel Prediction
https://github.com/liuchuang0059/structmae
공식 코드Where to Mask: Structure-Guided Masking for Graph Masked Autoencoders
https://github.com/liudakl/ParAIsite
공식 코드Transfer Learning for Deep Learning-based Prediction of Lattice Thermal Conductivity
https://github.com/liuj-2023/invtremo
공식 코드Bayesian Inverse Transfer in Evolutionary Multiobjective Optimization
https://github.com/liuji93/GTH
공식 코드Optimal Projection Guided Transfer Hashing for Image Retrieval
https://github.com/liuqidong07/moelora-peft
공식 코드When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications
https://github.com/liuqs111/DNA-SE
공식 코드DNA-SE: Towards Deep Neural-Nets Assisted Semiparametric Estimation
https://github.com/liuquande/MS-Net
공식 코드MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data
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