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Meta-Learning

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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/Superminionsfy/Personalized-Data-Generation-using-GAN

LEAF: A Benchmark for Federated Settings

https://github.com/Sypherd/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/TalSchuster/CrossLingualContextualEmb

Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing

https://github.com/Teddy00888/arn_mindspore

Few-shot Action Recognition with Permutation-invariant Attention

https://github.com/TejasAnvekar/Mahalanobis-k-NN

GPr-Net: Geometric Prototypical Network for Point Cloud Few-Shot Learning

https://github.com/TencentYoutuResearch/Adv-Makeup

Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition

https://github.com/ThomasMiconi/LearningToLearnBOHP

Learning to learn with backpropagation of Hebbian plasticity

https://github.com/TianWuYuJiangHenShou/LabelSemantics

Label Semantics for Few Shot Named Entity Recognition

https://github.com/Tikquuss/meta_XLM

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/TinfoilHat0/Learning-to-Reweight-Examples-for-Robust-Deep-Learning-with-PyTorch-Higher

Learning to Reweight Examples for Robust Deep Learning

https://github.com/TomerGalanti/InfiniteWidthHypernets

On Infinite-Width Hypernetworks

https://github.com/TsingZ0/PFL-Non-IID

Personalized Federated Learning with Moreau Envelopes

https://github.com/UIC-Liu-Lab/ContinualLM

Continual Training of Language Models for Few-Shot Learning

https://github.com/UTMIST/oneshot-data

The Omniglot challenge: a 3-year progress report

https://github.com/UTMIST/oneshot-rs

The Omniglot challenge: a 3-year progress report

https://github.com/UTS-AAi/autoweka

AutonoML: Towards an Integrated Framework for Autonomous Machine Learning

https://github.com/VE-FORBRYDERNE/mtj-softtuner

The Power of Scale for Parameter-Efficient Prompt Tuning

https://github.com/VachanVY/gpt.jax

Language Models are Few-Shot Learners

https://github.com/Vill-Lab/2024-TIP-MetaPrompt

Learning Domain Invariant Prompt for Vision-Language Models

https://github.com/ViswanathaReddyGajjala/FewShotWithoutForgetting

Dynamic Few-Shot Visual Learning without Forgetting

https://github.com/WangTianduo/Prototypical-Networks

Prototypical Networks for Few-shot Learning

https://github.com/WenbinLee/DN4

Matching Networks for One Shot Learning

https://github.com/YBZh/MetaFGNet

Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary Data

https://github.com/YDU-uva/MetaKernel

Learning to Learn Kernels with Variational Random Features

https://github.com/YabinZhang1994/MetaFGNet

Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary Data

https://github.com/YanFangCS/CyCTR-Pytorch

Few-Shot Segmentation via Cycle-Consistent Transformer

https://github.com/YanYan0716/MPL

Meta Pseudo Labels

https://github.com/Yingjun-Du/MetaVRF

Learning to Learn Kernels with Variational Random Features

https://github.com/YoungHector/one-shot-mann

One-shot Learning with Memory-Augmented Neural Networks

https://github.com/YoungXIAO13/FewShotDetection

Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild

https://github.com/YoungXIAO13/PoseContrast

Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild

https://github.com/Yuzhe-CHEN/NerfSNN

On First-Order Meta-Learning Algorithms

https://github.com/ZVK/Talking-Heads

Few-Shot Adversarial Learning of Realistic Neural Talking Head Models

https://github.com/ZVK/talking_heads

Few-Shot Adversarial Learning of Realistic Neural Talking Head Models

https://github.com/Zelgunn/MANN_tf_keras

One-shot Learning with Memory-Augmented Neural Networks

https://github.com/Zeng-WH/Prompt-Tuning

The Power of Scale for Parameter-Efficient Prompt Tuning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.