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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/JHA-Lab/impromptu

Im-Promptu: In-Context Composition from Image Prompts

https://github.com/JWSoh/MainSR

Meta-Transfer Learning for Zero-Shot Super-Resolution

https://github.com/JeonMinkyu/MAML_Pytorch

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

https://github.com/JeremyFisher/few_shot_3dr

Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors

https://github.com/JetBrains-Research/python-ml-type-hints

Typilus: Neural Type Hints

https://github.com/JiyangZhang/learning-to-reweight-in-relation-extraion

Learning to Reweight Examples for Robust Deep Learning

https://github.com/JohnKurian/TableGPT

Few-Shot NLG with Pre-Trained Language Model

https://github.com/JonasGeiping/poisoning-gradient-matching

MetaPoison: Practical General-purpose Clean-label Data Poisoning

https://github.com/Jun-CEN/Open-World-Semantic-Segmentation

Deep Metric Learning for Open World Semantic Segmentation

https://github.com/JunMa11/COVID-19-CT-Seg-Benchmark

Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation

https://github.com/JunjieYang97/stocBiO

Bilevel Optimization: Convergence Analysis and Enhanced Design

https://github.com/KabirSingh114/DeepFake_Face_Detection

FDFtNet: Facing Off Fake Images using Fake Detection Fine-tuning Network

https://github.com/KaiyangZhou/mixstyle-release

Domain Generalization: A Survey

https://github.com/KamalM8/Few-Shot-learning-Fashion

Matching Networks for One Shot Learning

https://github.com/KandariSrinivas/Adversarial-Feature-Hallucination-Networks-for-Few-Shot-Learning

Adversarial Feature Hallucination Networks for Few-Shot Learning

https://github.com/KarhouTam/FL-bench

Personalized Federated Learning with Moreau Envelopes

https://github.com/KarhouTam/FedRecon

Federated Reconstruction: Partially Local Federated Learning

https://github.com/KarhouTam/Personalized-FedAvg

Improving Federated Learning Personalization via Model Agnostic Meta Learning

https://github.com/KastanDay/video-pretrained-transformer

Prismer: A Vision-Language Model with Multi-Task Experts

https://github.com/Kaushalya/metal

MetAL: Active Semi-Supervised Learning on Graphs via Meta Learning

https://github.com/Kostis-S-Z/mrcl_re

Meta-Learning Representations for Continual Learning

https://github.com/LKLQQ/LEO

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

https://github.com/LeslieZhoa/DCT-NET.Pytorch

DCT-Net: Domain-Calibrated Translation for Portrait Stylization

https://github.com/LiangHann/USAR

Unsupervised Meta-learning of Figure-Ground Segmentation via Imitating Visual Effects

https://github.com/Lieberk/Paddle-FSL-Baseline

A Closer Look at Few-shot Classification

https://github.com/Lieberk/Paddle-FSL-GNN

Few-Shot Learning with Graph Neural Networks

https://github.com/Lieberk/Paddle-MatchingNet

Matching Networks for One Shot Learning

https://github.com/Lightning-AI/lit-llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/LindgeW/MetaAug4NER

Meta Soft Label Generation for Noisy Labels

https://github.com/LiuXinyu12378/MatchingNetworks

Matching Networks for One Shot Learning

https://github.com/LourisXu/rfs

Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?

https://github.com/MI2DataLab/elo-based-measure

Interpretable Meta-Measure for Model Performance

https://github.com/ML-Bee/associative-alignment-fs

Associative Alignment for Few-shot Image Classification

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/bit

Big Transfer (BiT): General Visual Representation Learning

https://github.com/MS-Mind/MS-Code-08/tree/main/meta-baseline

Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning

https://github.com/MS-P3/code5/tree/main/llama

LLaMA: Open and Efficient Foundation Language Models

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