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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/zzzx1224/singlesamplegeneralization-iclr2022

공식 코드

Learning to Generalize across Domains on Single Test Samples

https://gitlab.anu.edu.au/u6554606/eaen

공식 코드

Episode Adaptive Embedding Networks for Few-shot Learning

https://gitlab.com/bruno_viti/gpe_4_cardiac_fss

공식 코드

Gaussian Process Emulators for Few-Shot Segmentation in Cardiac MRI

https://gitlab.com/farid-fari/fewshot-learning

공식 코드

Few-shot learning through contextual data augmentation

https://gitlab.kitware.com/darpa_learn/learn

공식 코드

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning

https://huggingface.co/geolocal/StreetCLIP

공식 코드

Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization

https://huggingface.co/ibm/materials.smi-ted

공식 코드

A Large Encoder-Decoder Family of Foundation Models For Chemical Language

https://worksheets.codalab.org/worksheets/0x55ed347e3beb4fa6971d3a226e48fc92

공식 코드

Shaping Visual Representations with Language for Few-shot Classification

https://dagshub.com/Dean/BioBERT-DAGsHub

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

https://dagshub.com/arjun2000ashok/FSL-SSL

When Does Self-supervision Improve Few-shot Learning?

https://github.com/2006pmach/awesome-few-shot-object-detection

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

https://github.com/2023-MindSpore-1/ms-code-106

An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning

https://github.com/2023-MindSpore-1/ms-code-141

Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure Prediction

https://github.com/2023-MindSpore-1/ms-code-18/tree/main/MaskedFaceRecognition

Learning Meta Face Recognition in Unseen Domains

https://github.com/2023-MindSpore-1/ms-code-210/tree/main/CMT

CMT in TREC-COVID Round 2: Mitigating the Generalization Gaps from Web to Special Domain Search

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/relationnet

RelationNet2: Deep Comparison Columns for Few-Shot Learning

https://github.com/2023-MindSpore-1/ms-code-217/tree/main/unisiam

Self-Supervision Can Be a Good Few-Shot Learner

https://github.com/2023-MindSpore-1/ms-code-6

An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning

https://github.com/2023-MindSpore-1/ms-code-6/tree/main/CMT

CMT in TREC-COVID Round 2: Mitigating the Generalization Gaps from Web to Special Domain Search

https://github.com/2023-MindSpore-1/ms-code-7/tree/main/unisiam

Meta-Learning for Semi-Supervised Few-Shot Classification

https://github.com/2023-MindSpore-4/Code-5/tree/main/MAML

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

https://github.com/2023-MindSpore-4/Code-5/tree/main/MaskedFaceRecognition

Learning Meta Face Recognition in Unseen Domains

https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/2023-MindSpore-4/Code14/tree/main/RNA

Learning to Design RNA

https://github.com/2023-MindSpore-4/Code6/tree/main/RNA

Learning to Design RNA

https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/bit

Big Transfer (BiT): General Visual Representation Learning

https://github.com/7ZFG1/Semi-Supervised-Learning-Meta-Pseudo-Learning-

Meta Pseudo Labels

https://github.com/AIDefender/MyDiscor

DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction

https://github.com/AUCOHL/RTL-Repo

GPT-4 Technical Report

https://github.com/Abdulrahman-Adel/Skin-Cancer-Detection

RECOD Titans at ISIC Challenge 2017

https://github.com/AceChuse/DCN

Decoder Choice Network for Meta-Learning

https://github.com/AhmetSencan/MaskSplit-Self-supervised-Meta-learning-for-Few-shot-Semantic-Segmentation

MaskSplit: Self-supervised Meta-learning for Few-shot Semantic Segmentation

https://github.com/AlessandraFaria/autosklearn

Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning

https://github.com/Anish144/CausalStructureNeuralProcess

A Meta-Learning Approach to Bayesian Causal Discovery

https://github.com/AntheaLi/cs224nProject

Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining

https://github.com/AntreasAntoniou/DAGAN

Data Augmentation Generative Adversarial Networks

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