https://github.com/nust-machine-intelligence-laboratory/fecanet
공식 코드FECANet: Boosting Few-Shot Semantic Segmentation with Feature-Enhanced Context-Aware Network
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/nutcrtnk/leaprec
공식 코드Leaping Through Time with Gradient-based Adaptation for Recommendation
https://github.com/nvakhoa/superclass-fsis
공식 코드Instance-level Few-shot Learning with Class Hierarchy Mining
https://github.com/nvlabs/adaptive-prediction
공식 코드Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning
https://github.com/nvlabs/bongard-hoi
공식 코드Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions
https://github.com/nvlabs/prismer
공식 코드https://github.com/nvr-avg/adaptive-prediction
공식 코드Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning
https://github.com/nyquixt/cascadetta
공식 코드Adaptive Cascading Network for Continual Test-Time Adaptation
https://github.com/nyu-larx/attack_metarl
공식 코드Sampling Attacks on Meta Reinforcement Learning: A Minimax Formulation and Complexity Analysis
https://github.com/nyu-mlda/openabc
공식 코드Too Big to Fail? Active Few-Shot Learning Guided Logic Synthesis
https://github.com/nz0001na/mad_maf
공식 코드Fusion-based Few-Shot Morphing Attack Detection and Fingerprinting
https://github.com/observeai-research/nli-fsl
공식 코드Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent Detection
https://github.com/odabashi/LMs-in-News-Summarization
공식 코드Unraveling the Capabilities of Language Models in News Summarization
https://github.com/odegeasslbc/FastGAN-pytorch
공식 코드Learning Spatially-Adaptive Squeeze-Excitation Networks for Image Synthesis and Image Recognition
https://github.com/oezyurty/replm
공식 코드Document-Level In-Context Few-Shot Relation Extraction via Pre-Trained Language Models
https://github.com/ogroth/geeco
공식 코드Goal-Conditioned End-to-End Visuomotor Control for Versatile Skill Primitives
https://github.com/oist-cnru/ReMASTER
공식 코드https://github.com/ojss/samptransfer
공식 코드Self-Attention Message Passing for Contrastive Few-Shot Learning
https://github.com/olakalisz/adios
공식 코드https://github.com/olakalisz/antibody-shapers
공식 코드https://github.com/oli-wang/merec
공식 코드Meta-learning enhanced next POI recommendation by leveraging check-ins from auxiliary cities
https://github.com/ondrejbohdal/evograd
공식 코드EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization
https://github.com/ondrejbohdal/label-distillation
공식 코드Flexible Dataset Distillation: Learn Labels Instead of Images
https://github.com/ondrejbohdal/meta-calibration
공식 코드Meta-Calibration: Learning of Model Calibration Using Differentiable Expected Calibration Error
https://github.com/ondrejklejch/learning_to_adapt
공식 코드Speaker Adaptive Training using Model Agnostic Meta-Learning
https://github.com/oniani/llms-vote
공식 코드Large Language Models Vote: Prompting for Rare Disease Identification
https://github.com/open-compass/raml
공식 코드Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective
https://github.com/open-debin/bayesian_mqda
공식 코드Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition
https://github.com/openai/evals
공식 코드https://github.com/openai/gpt-3
공식 코드https://github.com/openai/mlsh
공식 코드https://github.com/openai/robosumo
공식 코드Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments
https://github.com/openai/supervised-reptile
공식 코드https://github.com/openlmlab/moss-rlhf
공식 코드Secrets of RLHF in Large Language Models Part II: Reward Modeling
https://github.com/oremnirv/taylorformer
공식 코드Taylorformer: Probabilistic Modelling for Random Processes including Time Series
https://github.com/oshapio/Efficient-Cross-Validation-of-Echo-State-Networks
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.