https://github.com/aerorobotics/neural-fly
공식 코드Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds
Papers with Code 아카이브
Meta-Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/afshinrahimi/mmner
공식 코드https://github.com/agosiewska/epp-meta-score
공식 코드https://github.com/ai-forever/mgpt
공식 코드https://github.com/aielawady/relic
공식 코드ReLIC: A Recipe for 64k Steps of In-Context Reinforcement Learning for Embodied AI
https://github.com/aijinrjinr/mlb-seg
공식 코드Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image Segmentation
https://github.com/aim-uofa/diffews
공식 코드Unleashing the Potential of the Diffusion Model in Few-shot Semantic Segmentation
https://github.com/aim-uofa/matcher
공식 코드Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching
https://github.com/aioz-ai/MICCAI19-MedVQA
공식 코드Overcoming Data Limitation in Medical Visual Question Answering
https://github.com/aioz-ai/MICCAI21_MMQ
공식 코드Multiple Meta-model Quantifying for Medical Visual Question Answering
https://github.com/airi-institute/asi-certification
공식 코드Certification of Speaker Recognition Models to Additive Perturbations
https://github.com/ait-assistive-autonomous-systems/hopomop
공식 코드https://github.com/ajfisch/few-shot-cp
공식 코드https://github.com/akariasai/attempt
공식 코드ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts
https://github.com/akaxlh/mb-gmn
공식 코드https://github.com/akoksal/meal
공식 코드https://github.com/albertqjiang/mma
공식 코드https://github.com/aldrich2y/mianet
공식 코드MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation
https://github.com/alessandraperniciano/meta-learning-strategy-fair-provider-exposure
공식 코드A Cost-Sensitive Meta-Learning Strategy for Fair Provider Exposure in Recommendation
https://github.com/alessiabertugli/FUSION
공식 코드Few-Shot Unsupervised Continual Learning through Meta-Examples
https://github.com/alex-shilei/mshnet
공식 코드Multi-similarity based Hyperrelation Network for few-shot segmentation
https://github.com/alexandertornede/as_on_a_meta_level
공식 코드https://github.com/alexandertornede/extreme_algorithm_selection
공식 코드Extreme Algorithm Selection With Dyadic Feature Representation
https://github.com/alexgoldie/rl-learned-optimization
공식 코드Can Learned Optimization Make Reinforcement Learning Less Difficult?
https://github.com/alexluedtke12/amc-meta-learning-of-optimal-prediction-procedures
공식 코드Adversarial Monte Carlo Meta-Learning of Optimal Prediction Procedures
https://github.com/alfassy/FETA
공식 코드FETA: Towards Specializing Foundation Models for Expert Task Applications
https://github.com/alibaba-mmai-research/hyrsmplusplus
공식 코드HyRSM++: Hybrid Relation Guided Temporal Set Matching for Few-shot Action Recognition
https://github.com/alibaba/EasyNLP
공식 코드Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains
https://github.com/alibaba/federatedscope
공식 코드https://github.com/alic-li/blackgoose_rimer
공식 코드https://github.com/alinlab/L2T-ww
공식 코드https://github.com/alinlab/psco
공식 코드Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive Learning
https://github.com/alisafaya/neurocache
공식 코드Neurocache: Efficient Vector Retrieval for Long-range Language Modeling
https://github.com/allenai/better-promptability
공식 코드Continued Pretraining for Better Zero- and Few-Shot Promptability
https://github.com/allenai/ethical-interventions
공식 코드Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions?
https://github.com/allenai/flex
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.