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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/yli1/CGPS

공식 코드

Compositional Generalization for Primitive Substitutions

https://github.com/yliuaa/bnmr-faircompface

공식 코드

Component-Based Fairness in Face Attribute Classification with Bayesian Network-informed Meta Learning

https://github.com/ymhe12/GS-Phong

공식 코드

GS-Phong: Meta-Learned 3D Gaussians for Relightable Novel View Synthesis

https://github.com/ynanwu/metagcd

공식 코드

MetaGCD: Learning to Continually Learn in Generalized Category Discovery

https://github.com/yongchao97/FRePo

공식 코드

Dataset Distillation using Neural Feature Regression

https://github.com/yongchao97/diffusion_inversion

공식 코드

Training on Thin Air: Improve Image Classification with Generated Data

https://github.com/yoonholee/MT-net

공식 코드

Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace

https://github.com/yoonholee/neural-complexity

공식 코드

Neural Complexity Measures

https://github.com/youjiangxu/FaMUS

공식 코드

Faster Meta Update Strategy for Noise-Robust Deep Learning

https://github.com/young-excavator/meta_lsm

공식 코드

Meta-learning an Intermediate Representation for Few-shot Block-wise Prediction of Landslide Susceptibility

https://github.com/youngjae-cho/app

공식 코드

Make Prompts Adaptable: Bayesian Modeling for Vision-Language Prompt Learning with Data-Dependent Prior

https://github.com/youthhoo/afa_for_few_shot_learning

공식 코드

Adversarial Feature Augmentation for Cross-domain Few-shot Classification

https://github.com/yuanpengtu/DMLP

공식 코드

Learning from Noisy Labels with Decoupled Meta Label Purifier

https://github.com/yue-zhongqi/ifsl

공식 코드

Interventional Few-Shot Learning

https://github.com/yue-zhongqi/tif

공식 코드

Few-shot Learner Parameterization by Diffusion Time-steps

https://github.com/yueeeeeeee/metaadapt

공식 코드

MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning

https://github.com/yueeeeeeee/metaevent

공식 코드

Zero- and Few-Shot Event Detection via Prompt-Based Meta Learning

https://github.com/yuhangzang/upt

공식 코드

Unified Vision and Language Prompt Learning

https://github.com/yuhao318/btm

공식 코드

Reviving Undersampling for Long-Tailed Learning

https://github.com/yukang-lin/rger

공식 코드

Reasoning Graph Enhanced Exemplars Retrieval for In-Context Learning

https://github.com/yul091/graphlogad

공식 코드

GLAD: Content-aware Dynamic Graphs For Log Anomaly Detection

https://github.com/yumeng5/fewgen

공식 코드

Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning

https://github.com/yumeng5/supergen

공식 코드

Generating Training Data with Language Models: Towards Zero-Shot Language Understanding

https://github.com/yushi-hu/ic-dst

공식 코드

In-Context Learning for Few-Shot Dialogue State Tracking

https://github.com/yuvenduan/plasticrnns

공식 코드

Hebbian and Gradient-based Plasticity Enables Robust Memory and Rapid Learning in RNNs

https://github.com/yuweisunn/homogeneous-learning

공식 코드

Homogeneous Learning: Self-Attention Decentralized Deep Learning

https://github.com/yuyinzhou/l2b

공식 코드

L2B: Learning to Bootstrap Robust Models for Combating Label Noise

https://github.com/yvogao/laqda

공식 코드

Improve Meta-learning for Few-Shot Text Classification with All You Can Acquire from the Tasks

https://github.com/yxxx616/platmetax

공식 코드

PlatMetaX: An Integrated MATLAB platform for Meta-Black-Box Optimization

https://github.com/yzhao062/elect

공식 코드

Toward Unsupervised Outlier Model Selection

https://github.com/yzhao062/metaod

공식 코드

Automating Outlier Detection via Meta-Learning

https://github.com/z3n0e/MFRW

공식 코드

Learning advisor networks for noisy image classification

https://github.com/zaplm/dc-sam

공식 코드

DC-SAM: In-Context Segment Anything in Images and Videos via Dual Consistency

https://github.com/zbf1991/SCL

공식 코드

Self-Guided and Cross-Guided Learning for Few-Shot Segmentation

https://github.com/zcrwind/tgg-pytorch

공식 코드

TGG: Transferable Graph Generation for Zero-shot and Few-shot Learning

https://github.com/zdsy/episode-specific-ft

공식 코드

Episode-specific Fine-tuning for Metric-based Few-shot Learners with Optimization-based Training

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