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/leaf-ai/muir
공식 코드Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains
https://github.com/leanderweber/layerwise-feedback-propagation
공식 코드Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation
https://github.com/learnables/learn2learn
공식 코드https://github.com/learningmatter-mit/geom
공식 코드GEOM: Energy-annotated molecular conformations for property prediction and molecular generation
https://github.com/learningtitans/cvpr-skin-solo-ensemble
공식 코드Solo or Ensemble? Choosing a CNN Architecture for Melanoma Classification
https://github.com/learningtitans/data-depth-design
공식 코드Data, Depth, and Design: Learning Reliable Models for Skin Lesion Analysis
https://github.com/leedaeuni/Temporal-Symptom-Aware-Multitask-Learning-KDD23
공식 코드Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning
https://github.com/leeroo-ai/leeroo_orchestrator
공식 코드Routoo: Learning to Route to Large Language Models Effectively
https://github.com/lehmannfa/mifno
공식 코드Multiple-Input Fourier Neural Operator (MIFNO) for source-dependent 3D elastodynamics
https://github.com/leia-llm/leia
공식 코드https://github.com/leirebv/synthetic_histopathology_dataset
공식 코드Unleashing the Potential of Synthetic Images: A Study on Histopathology Image Classification
https://github.com/leiunnn/transferlearningbasedcebydnsp
공식 코드https://github.com/lely475/ctplab_semicol2023
공식 코드https://github.com/leminhbinh0209/add
공식 코드https://github.com/leo-chu/semanticloc
공식 코드https://github.com/leonsuarez24/dkdcis
공식 코드Distilling Knowledge for Designing Computational Imaging Systems
https://github.com/lephuoccat/Fisher-Information-NAS
공식 코드Fisher Task Distance and Its Application in Neural Architecture Search
https://github.com/lephuoccat/log-det-distance-NAS
공식 코드https://github.com/lesley222/HJAILab-OPEN
공식 코드RL-I2IT: Image-to-Image Translation with Deep Reinforcement Learning
https://github.com/levinroman/tabular-transfer-learning
공식 코드https://github.com/lexieyang/3d-pointzshots
공식 코드https://github.com/lexisnexis-risk-open-source/simltd
공식 코드SimLTD: Simple Supervised and Semi-Supervised Long-Tailed Object Detection
https://github.com/leyadev/vocabulary-transfer
공식 코드https://github.com/lhc1224/cross-view-affordance-grounding
공식 코드https://github.com/lhc1224/cross-view-ag
공식 코드https://github.com/lhf-labs/finance-news-analysis-bert
공식 코드https://github.com/lhfazry/spin-summ
공식 코드https://github.com/lhkhiem28/Learning-for-Amalgamation
공식 코드Learning for Amalgamation: A Multi-Source Transfer Learning Framework For Sentiment Classification
https://github.com/lhmtriet/deepcva
공식 코드DeepCVA: Automated Commit-level Vulnerability Assessment with Deep Multi-task Learning
https://github.com/lhoangan/multas
공식 코드https://github.com/lhoyer/improving_segmentation_with_selfsupervised_depth
공식 코드https://github.com/lhtie/bio-domain-transfer
공식 코드Named Entity Recognition Under Domain Shift via Metric Learning for Life Sciences
https://github.com/li-xirong/ude
공식 코드https://github.com/liangcaisu/stem
공식 코드STEM: Unleashing the Power of Embeddings for Multi-task Recommendation
https://github.com/liangzid/MERGE
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.