https://github.com/jingbang-chen/mixup-meta-protein-simulators
공식 코드Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein Simulators
Papers with Code 아카이브
Meta-Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
- 4
- 보존된 평가 기록
- 13
Meta-learning is a methodology considered with "learning to learn" machine learning algorithms.
<span style="color:grey; opacity: 0.6">( Image credit: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jingminsun/lemon_prose
공식 코드https://github.com/jingw193/adaptive_fss
공식 코드Adaptive FSS: A Novel Few-Shot Segmentation Framework via Prototype Enhancement
https://github.com/jingw193/adaptivefss
공식 코드Adaptive FSS: A Novel Few-Shot Segmentation Framework via Prototype Enhancement
https://github.com/jinhualiang/apt
공식 코드Acoustic Prompt Tuning: Empowering Large Language Models with Audition Capabilities
https://github.com/jinxu06/metafun-tensorflow
공식 코드https://github.com/jixuan-wang/grad2task
공식 코드Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation
https://github.com/jjongyn/cml
공식 코드https://github.com/jlab-nlp/refpydst
공식 코드Diverse Retrieval-Augmented In-Context Learning for Dialogue State Tracking
https://github.com/jlager/few-shot-leaf-segmentation
공식 코드Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa
https://github.com/jlianglab/ace
공식 코드ACE: Anatomically Consistent Embeddings in Composition and Decomposition
https://github.com/jlinekai/clover
공식 코드Cost-effective Instruction Learning for Pathology Vision and Language Analysis
https://github.com/joakimjohnander/dgpnet
공식 코드https://github.com/joeljosephjin/La-MAML
공식 코드Reproducibility Report: La-MAML: Look-ahead Meta Learning for Continual Learning
https://github.com/joey-wang123/imbalancemeta
공식 코드Meta Learning on a Sequence of Imbalanced Domains with Difficulty Awareness
https://github.com/joffery/M-ADA
공식 코드https://github.com/john-x-jiang/epnn
공식 코드https://github.com/johncai117/DAMSL
공식 코드https://github.com/johnjaejunlee95/xb-maml
공식 코드XB-MAML: Learning Expandable Basis Parameters for Effective Meta-Learning with Wide Task Coverage
https://github.com/johnmaijer/x-few-_cd-fsod
공식 코드NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results
https://github.com/johswald/learning_where_to_learn
공식 코드Learning where to learn: Gradient sparsity in meta and continual learning
https://github.com/jonasrothfuss/meta_learning_pacoh
공식 코드https://github.com/jonasrothfuss/promp
공식 코드https://github.com/jorgegus/autotext
공식 코드https://github.com/joyjayng/Bongard-OpenWorld
공식 코드Bongard-OpenWorld: Few-Shot Reasoning for Free-form Visual Concepts in the Real World
https://github.com/jsjs0369/MEBQAT
공식 코드Bitwidth-Adaptive Quantization-Aware Neural Network Training: A Meta-Learning Approach
https://github.com/juglab/DenoiSeg
공식 코드https://github.com/juhongm999/hsnet
공식 코드https://github.com/juliuswang-7/encoderreg
공식 코드Encoding Matching Criteria for Cross-domain Deformable Image Registration
https://github.com/junbao-zhou/track-no-forgetting
공식 코드TeFF: Tracking-enhanced Forgetting-free Few-shot 3D LiDAR Semantic Segmentation
https://github.com/june01/wfsal-icmr21
공식 코드https://github.com/junha-kim/Learning-to-Adapt-to-Unseen-Abnormal-Activities
공식 코드Learning to Adapt to Unseen Abnormal Activities under Weak Supervision
https://github.com/junjieyang97/meta-controlnet
공식 코드Meta ControlNet: Enhancing Task Adaptation via Meta Learning
https://github.com/junjieyang97/stocbio_hp
공식 코드Bilevel Optimization: Convergence Analysis and Enhanced Design
https://github.com/junjun-jiang/BaMBNet
공식 코드BaMBNet: A Blur-aware Multi-branch Network for Defocus Deblurring
https://github.com/junshern/few-shot-adaptation
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.