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Meta-Learning
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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/suneelbelkhale/model-based-meta-rl-for-flight
공식 코드Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads
https://github.com/sung-won-kim/teg
공식 코드https://github.com/sungnyun/understanding-cdfsl
공식 코드Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty
https://github.com/sungwon-han/featllm
공식 코드Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning
https://github.com/sunhaozhe/omniprint
공식 코드https://github.com/sunhaozhe/omniprint-datasets
공식 코드https://github.com/sunyue93/rep-learning
공식 코드https://github.com/super-shayan/feml
공식 코드https://github.com/sustainable-computing/blinder
공식 코드Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated Learning
https://github.com/sutd-visual-computing-group/KML-Classification
공식 코드Revisit Multimodal Meta-Learning through the Lens of Multi-Task Learning
https://github.com/suvarnak/generativefslcovid
공식 코드Generative Transfer Learning: Covid-19 Classification with a few Chest X-ray Images
https://github.com/symanto-research/active-few-shot-learning
공식 코드https://github.com/symanto-research/few-shot-learning-label-tuning
공식 코드https://github.com/syp2ysy/SVF
공식 코드Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning
https://github.com/syp2ysy/prompt-self
공식 코드Exploring Effective Factors for Improving Visual In-Context Learning
https://github.com/syp2ysy/vrp-sam
공식 코드https://github.com/sz128/few_shot_slot_tagging_and_NER
공식 코드Vector Projection Network for Few-shot Slot Tagging in Natural Language Understanding
https://github.com/szc12153/sparse_meta_tuning
공식 코드Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts
https://github.com/tacju/partimagenet
공식 코드https://github.com/taconite/MetaAvatar-release
공식 코드MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images
https://github.com/tailintalent/mela
공식 코드https://github.com/tanay2001/core
공식 코드CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation
https://github.com/tancik/learnit
공식 코드Learned Initializations for Optimizing Coordinate-Based Neural Representations
https://github.com/tanganke/subspace_fusion
공식 코드Concrete Subspace Learning based Interference Elimination for Multi-task Model Fusion
https://github.com/tankche1/IDeMe-Net
공식 코드https://github.com/tankche1/Semantic-Feature-Augmentation-in-Few-shot-Learning
공식 코드Multi-level Semantic Feature Augmentation for One-shot Learning
https://github.com/tanmdl/scale
공식 코드https://github.com/tanyuqian/redco
공식 코드RedCoast: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs
https://github.com/tata1661/FewShotPapers
공식 코드Generalizing from a Few Examples: A Survey on Few-Shot Learning
https://github.com/taveraantonio/pixda
공식 코드Pixel-by-Pixel Cross-Domain Alignment for Few-Shot Semantic Segmentation
https://github.com/tcbenchstack/tcbench
공식 코드https://github.com/tdopierre/ProtAugment
공식 코드ProtAugment: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning
https://github.com/tejasanvekar/gpr-net
공식 코드GPr-Net: Geometric Prototypical Network for Point Cloud Few-Shot Learning
https://github.com/temu-bsc/gpt3-queries
공식 코드On the Multilingual Capabilities of Very Large-Scale English Language Models
https://github.com/tennisonliu/llambo
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.