Papers with Code 아카이브

Meta-Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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/eyalmichaeli/saspa-aug

공식 코드

Advancing Fine-Grained Classification by Structure and Subject Preserving Augmentation

https://github.com/f-god6/ilmar

공식 코드

Imitation Learning from Suboptimal Demonstrations via Meta-Learning An Action Ranker

https://github.com/f3rm/f3rm

공식 코드

Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation

https://github.com/facebookresearch/SentAugment

공식 코드

Self-training Improves Pre-training for Natural Language Understanding

https://github.com/facebookresearch/adversarially-motivated-intrinsic-goals

공식 코드

Learning with AMIGo: Adversarially Motivated Intrinsic Goals

https://github.com/facebookresearch/amortized-optimization-tutorial

공식 코드

Tutorial on amortized optimization

https://github.com/facebookresearch/atlas

공식 코드

Atlas: Few-shot Learning with Retrieval Augmented Language Models

https://github.com/facebookresearch/electra-fewshot-learning

공식 코드

Prompting ELECTRA: Few-Shot Learning with Discriminative Pre-Trained Models

https://github.com/facebookresearch/higher

공식 코드

Generalized Inner Loop Meta-Learning

https://github.com/facebookresearch/lawt

공식 코드

Linear algebra with transformers

https://github.com/facebookresearch/llama

공식 코드

LLaMA: Open and Efficient Foundation Language Models

https://github.com/facebookresearch/metaicl

공식 코드

MetaICL: Learning to Learn In Context

https://github.com/facebookresearch/neural-scs

공식 코드

Neural Fixed-Point Acceleration for Convex Optimization

https://github.com/facebookresearch/noisy_few_shot

공식 코드

Few-shot Learning with Noisy Labels

https://github.com/facebookresearch/perfect

공식 코드

PERFECT: Prompt-free and Efficient Few-shot Learning with Language Models

https://github.com/fangkaipeng/pros

공식 코드

ProS: Prompting-to-simulate Generalized knowledge for Universal Cross-Domain Retrieval

https://github.com/fangshuman/LLTA

공식 코드

Learning to Learn Transferable Attack

https://github.com/fanq15/FewX

공식 코드

Few-Shot Video Object Detection

https://github.com/fanq15/ssp

공식 코드

Self-Support Few-Shot Semantic Segmentation

https://github.com/faresbougourzi/awesome-dl-for-medical-imaging-segmentation

공식 코드

Recent Advances in Medical Imaging Segmentation: A Survey

https://github.com/fatemehazimi990/RS2S

공식 코드

Revisiting Sequence-to-Sequence Video Object Segmentation with Multi-Task Loss and Skip-Memory

https://github.com/fatemehtd/Echo-SyncNet

공식 코드

Echo-SyncNet: Self-supervised Cardiac View Synchronization in Echocardiography

https://github.com/faziletgokbudak/One-shot-Photo-Retouching

공식 코드

One-shot Detail Retouching with Patch Space Neural Transformation Blending

https://github.com/fc-liu/mcmn

공식 코드

Pre-training to Match for Unified Low-shot Relation Extraction

https://github.com/fcdl94/fss

공식 코드

Prototype-based Incremental Few-Shot Semantic Segmentation

https://github.com/few-shot-learning-on-chest-x-ray/fsl_subspace

공식 코드

Few-shot Diagnosis of Chest x-rays Using an Ensemble of Random Discriminative Subspaces

https://github.com/fietelab/inductive-bias-complexity

공식 코드

Model-agnostic Measure of Generalization Difficulty

https://github.com/filyp/mudman

공식 코드

Robust LLM Unlearning with MUDMAN: Meta-Unlearning with Disruption Masking And Normalization

https://github.com/firasl/BoCF

공식 코드

INTEL-TAU: A Color Constancy Dataset

https://github.com/fiveai/on-episodes-fsl

공식 코드

On Episodes, Prototypical Networks, and Few-shot Learning

https://github.com/flagopen/flagembedding

공식 코드

Long Context Compression with Activation Beacon

https://github.com/flairox/jaxmarl

공식 코드

JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

https://github.com/flennerhag/warpgrad

공식 코드

Meta-Learning with Warped Gradient Descent

https://github.com/flitternie/graphq_ir

공식 코드

GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation

https://github.com/fmx789/meta-hmr

공식 코드

Incorporating Test-Time Optimization into Training with Dual Networks for Human Mesh Recovery

https://github.com/forgi86/sysid-neural-manifold

공식 코드

Manifold meta-learning for reduced-complexity neural system identification

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