https://github.com/liuyuanwei98/ipmt
공식 코드Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation
Papers with Code 아카이브
Meta-Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 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/liuzechun/MetaPruning
공식 코드MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning
https://github.com/livingingroups/animal2vec
공식 코드https://github.com/livreq/meta-sgld
공식 코드Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis
https://github.com/lixinustc/casual-ir-dil
공식 코드Learning Distortion Invariant Representation for Image Restoration from A Causality Perspective
https://github.com/lixinustc/causal-ir-dil
공식 코드Learning Distortion Invariant Representation for Image Restoration from A Causality Perspective
https://github.com/lizhecheng02/vlm4font
공식 코드Texture or Semantics? Vision-Language Models Get Lost in Font Recognition
https://github.com/lizhh268/FSSUWNet
공식 코드https://github.com/llan-ml/MetaTNE
공식 코드Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding
https://github.com/llan-ml/tesp
공식 코드Meta Reinforcement Learning with Task Embedding and Shared Policy
https://github.com/lmsdss/metamodulation
공식 코드MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer Tasks
https://github.com/lmzintgraf/cavia
공식 코드https://github.com/lmzintgraf/hyperx
공식 코드Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning
https://github.com/lmzintgraf/varibad
공식 코드VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning
https://github.com/loadder/ett_tmlr2022
공식 코드Exploring Efficient Few-shot Adaptation for Vision Transformers
https://github.com/locuslab/flyp
공식 코드Finetune like you pretrain: Improved finetuning of zero-shot vision models
https://github.com/locuslab/jiio-deq
공식 코드Joint inference and input optimization in equilibrium networks
https://github.com/locuslab/tta_conjugate
공식 코드https://github.com/lorenmt/auto-lambda
공식 코드https://github.com/lorenmt/maxl
공식 코드https://github.com/louisegan514/hidden-entity-detection-from-github-leveraging-llms
공식 코드Hidden Entity Detection from GitHub Leveraging Large Language Models
https://github.com/lovelyqian/AMeFu-Net
공식 코드Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition
https://github.com/lovelyqian/CDFSOD-benchmark
공식 코드Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector
https://github.com/lovelyqian/ME-D2N_for_CDFSL
공식 코드ME-D2N: Multi-Expert Domain Decompositional Network for Cross-Domain Few-Shot Learning
https://github.com/lovelyqian/Meta-FDMixup
공식 코드Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data
https://github.com/lovelyqian/ntire2025_cdfsod
공식 코드NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results
https://github.com/lovelyqian/styleadv-cdfsl
공식 코드StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning
https://github.com/lovelyqian/wave-SAN-CDFSL
공식 코드Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning
https://github.com/lraszewski/wiki-socks
공식 코드https://github.com/ltian678/metatroll-code
공식 코드MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters
https://github.com/lucashancef/e-sort
공식 코드https://github.com/lucashancef/fafesort
공식 코드https://github.com/lucaslingle/metavb
공식 코드https://github.com/lucfra/FAR-HO
공식 코드A Bridge Between Hyperparameter Optimization and Learning-to-learn
https://github.com/luchris429/popjaxrl
공식 코드Structured State Space Models for In-Context Reinforcement Learning
https://github.com/luchris429/s5rl
공식 코드Structured State Space Models for In-Context Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.