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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/Yikai-Wang/ICI-FSL

공식 코드

Instance Credibility Inference for Few-Shot Learning

https://github.com/YingzhenLi/SteinGrad

공식 코드

Gradient Estimators for Implicit Models

https://github.com/YujiaBao/Distributional-Signatures

공식 코드

Few-shot Text Classification with Distributional Signatures

https://github.com/Yussef93/FewShotCellSegmentation

공식 코드

Few-Shot Microscopy Image Cell Segmentation

https://github.com/Yuxin-CV/DTN

공식 코드

Diversity Transfer Network for Few-Shot Learning

https://github.com/ZJU-DAILY/SGDL

공식 코드

Self-Guided Learning to Denoise for Robust Recommendation

https://github.com/ZPDu/Domain-general-Crowd-Counting-in-Unseen-Scenarios

공식 코드

Domain-General Crowd Counting in Unseen Scenarios

https://github.com/Ze-Yang/Context-Transformer

공식 코드

Context-Transformer: Tackling Object Confusion for Few-Shot Detection

https://github.com/ZhangGongjie/Meta-DETR

공식 코드

Meta-DETR: Image-Level Few-Shot Object Detection with Inter-Class Correlation Exploitation

https://github.com/ZhangYuanhan-AI/NOAH

공식 코드

Neural Prompt Search

https://github.com/ZhaoZhibin/DL-based-Intelligent-Diagnosis-Benchmark

공식 코드

Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study

https://github.com/Zhen-ao/BSDB-CDFSL_Track

공식 코드

Ensemble Model with Batch Spectral Regularization and Data Blending for Cross-Domain Few-Shot Learning with Unlabeled Data

https://github.com/Zheng-Meng/Meta-learning-Ecosystems

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Learning to learn ecosystems from limited data -- a meta-learning approach

https://github.com/ZhexiongLiu/Task-Adaptive-Meta-Learning

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Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizability

https://github.com/ZhiningLiu1998/mesa

공식 코드

MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler

https://github.com/ZhixiuYe/MLMAN

공식 코드

Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification

https://github.com/ZihaoWang/Few-shot-KGC

공식 코드

Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion

https://github.com/ZihaoZhao/SpikeGatingFlow

공식 코드

The Spike Gating Flow: A Hierarchical Structure Based Spiking Neural Network for Online Gesture Recognition

https://github.com/a-moin/flexemg

공식 코드

An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier

https://github.com/aHapBean/PCP-MAE

공식 코드

PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders

https://github.com/abdelfattah-lab/nasflat_latency

공식 코드

On Latency Predictors for Neural Architecture Search

https://github.com/abdullahjamal/Longtail_DA

공식 코드

Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective

https://github.com/abdumaa/hiqualprop

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HQP: A Human-Annotated Dataset for Detecting Online Propaganda

https://github.com/abhi-iyer/meta-learning

공식 코드

Collision Avoidance Robotics Via Meta-Learning (CARML)

https://github.com/abhi4ssj/few-shot-segmentation

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'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images

https://github.com/abrazinskas/FewSum

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Few-Shot Learning for Opinion Summarization

https://github.com/abrazinskas/selsum

공식 코드

Learning Opinion Summarizers by Selecting Informative Reviews

https://github.com/acbull/HiCE

공식 코드

Few-Shot Representation Learning for Out-Of-Vocabulary Words

https://github.com/acctouhou/frankenstein_optimizer

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Frankenstein Optimizer: Harnessing the Potential by Revisiting Optimization Tricks

https://github.com/acewjh/fsfg

공식 코드

Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-Learning

https://github.com/adamjelley/poem

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Contrastive Meta-Learning for Partially Observable Few-Shot Learning

https://github.com/adapter-hub/playground

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AdapterHub Playground: Simple and Flexible Few-Shot Learning with Adapters

https://github.com/adipandas/one-shot-steel-surfaces

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One-Shot Recognition of Manufacturing Defects in Steel Surfaces

https://github.com/adishs/neurips2021_explicable-reward-design_code

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Reward Design for Reinforcement Learning Agents

https://github.com/adriangonz/statistical-nlp-17

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One-Shot Learning for Language Modelling

https://github.com/ae-foster/cresp

공식 코드

On Contrastive Representations of Stochastic Processes

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