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/allenai/mmc4

공식 코드

Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with Text

https://github.com/allenai/savn

공식 코드

Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning

https://github.com/allenai/unifew

공식 코드

FLEX: Unifying Evaluation for Few-Shot NLP

https://github.com/allenhaozhu/protolp

공식 코드

Transductive Few-shot Learning with Prototype-based Label Propagation by Iterative Graph Refinement

https://github.com/alon-albalak/flad

공식 코드

Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data

https://github.com/alrope123/z-icl

공식 코드

Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations

https://github.com/alshedivat/lola

공식 코드

DiCE: The Infinitely Differentiable Monte-Carlo Estimator

https://github.com/amanpriyanshu/adaptkeybert

공식 코드

AdaptKeyBERT: An Attention-Based approach towards Few-Shot & Zero-Shot Domain Adaptation of KeyBERT

https://github.com/amazingren/point-cmae

공식 코드

Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised Learning

https://github.com/amazon-research/dse

공식 코드

Learning Dialogue Representations from Consecutive Utterances

https://github.com/amazon-research/few-shot-object-detection-benchmark

공식 코드

Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark

https://github.com/amazon-research/meta-learning-the-difference

공식 코드

Meta-Learning the Difference: Preparing Large Language Models for Efficient Adaptation

https://github.com/amazon-science/few-shot-baseline

공식 코드

A Baseline for Few-Shot Image Classification

https://github.com/amazon-science/instruction-tuning-for-absa

공식 코드

Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis

https://github.com/amazon-science/uniform-episodic-sampling

공식 코드

Uniform Sampling over Episode Difficulty

https://github.com/amgao/rls-onlinetrack

공식 코드

Recursive Least-Squares Estimator-Aided Online Learning for Visual Tracking

https://github.com/amiiir-sarfi/seal

공식 코드

Simulated Annealing in Early Layers Leads to Better Generalization

https://github.com/amingwu/up-fsod

공식 코드

Universal-Prototype Enhancing for Few-Shot Object Detection

https://github.com/amink8/folk

공식 코드

Frequency-Guided Masking for Enhanced Vision Self-Supervised Learning

https://github.com/amirbar/detreg

공식 코드

DETReg: Unsupervised Pretraining with Region Priors for Object Detection

https://github.com/amirrezafateh/msdnet

공식 코드

MSDNet: Multi-Scale Decoder for Few-Shot Semantic Segmentation via Transformer-Guided Prototyping

https://github.com/amirziai/rafic

공식 코드

RAFIC: Retrieval-Augmented Few-shot Image Classification

https://github.com/amitjena1992/meta-nlf

공식 코드

Meta-Learning-Based Adaptive Stability Certificates for Dynamical Systems

https://github.com/amzn/xfer

공식 코드

Transferring Knowledge across Learning Processes

https://github.com/anaryegen/few_shot_argument_mining

공식 코드

Argument Mining in Data Scarce Settings: Cross-lingual Transfer and Few-shot Techniques

https://github.com/andreamad8/FSB

공식 코드

Few-Shot Bot: Prompt-Based Learning for Dialogue Systems

https://github.com/andrewbo29/mtm-meta-learning-sa

공식 코드

Simultaneous Perturbation Method for Multi-Task Weight Optimization in One-Shot Meta-Learning

https://github.com/anfeather/eigen-reptile

공식 코드

Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile

https://github.com/angtian/neuralvs

공식 코드

Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose

https://github.com/angusmaiden/complex-fer

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Complex Facial Expression Recognition Using Deep Knowledge Distillation of Basic Features

https://github.com/anindyasarkariith/psrl_vas

공식 코드

A Partially Supervised Reinforcement Learning Framework for Visual Active Search

https://github.com/anish-lu-yihe/pySVRT

공식 코드

Program synthesis performance constrained by non-linear spatial relations in Synthetic Visual Reasoning Test

https://github.com/anishmadan23/B-SMALL

공식 코드

B-SMALL: A Bayesian Neural Network approach to Sparse Model-Agnostic Meta-Learning

https://github.com/annaVettoruzzo/L2L_with_attention

공식 코드

Learning to Learn without Forgetting using Attention

https://github.com/annaproxy/udify-metalearning

공식 코드

Meta-Learning for Fast Cross-Lingual Adaptation in Dependency Parsing

https://github.com/annavettoruzzo/cafeme

공식 코드

Personalized Federated Learning with Contextual Modulation and Meta-Learning

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