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/corl-team/xland-minigrid

공식 코드

XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX

https://github.com/corne00/ddunetforheatplumeprediction

공식 코드

Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport

https://github.com/cos4007/grape_demonstrations

공식 코드

Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehension

https://github.com/coteries/cedille-ai

공식 코드

Cedille: A large autoregressive French language model

https://github.com/cownowan/dass

공식 코드

Meta-prediction Model for Distillation-Aware NAS on Unseen Datasets

https://github.com/cpphoo/STARTUP

공식 코드

Self-training for Few-shot Transfer Across Extreme Task Differences

https://github.com/cps11/darl

공식 코드

Reinforcement Learning for Few-Shot Text Generation Adaptation

https://github.com/crisostomi/metric-few-shot-graph

공식 코드

Metric Based Few-Shot Graph Classification

https://github.com/cser-tang-hao/bsfa-fsfg

공식 코드

Boosting Few-shot Fine-grained Recognition with Background Suppression and Foreground Alignment

https://github.com/cshhzhao/gcope

공식 코드

All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining

https://github.com/csiro-robotics/mdl

공식 코드

Exploiting Field Dependencies for Learning on Categorical Data

https://github.com/csml-iit-ucl/bioptexps

공식 코드

Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-start

https://github.com/csuhan/vfa

공식 코드

Few-Shot Object Detection via Variational Feature Aggregation

https://github.com/csyanbin/TPN-pytorch

공식 코드

Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning

https://github.com/ctom2/latent-space-transform

공식 코드

Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural network

https://github.com/cuong-pv/MetaAug

공식 코드

MetaAug: Meta-Data Augmentation for Post-Training Quantization

https://github.com/cuong-pv/MetaAug-PTQ

공식 코드

MetaAug: Meta-Data Augmentation for Post-Training Quantization

https://github.com/cutz-j/FDFtNet

공식 코드

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

https://github.com/cv516buaa/sd-aanet

공식 코드

A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation

https://github.com/cvjena/semantic-embeddings

공식 코드

Hierarchy-based Image Embeddings for Semantic Image Retrieval

https://github.com/cvl-hub/gprn

공식 코드

SAM-Aware Graph Prompt Reasoning Network for Cross-Domain Few-Shot Segmentation

https://github.com/cvlab-stonybrook/fsl-rsvae

공식 코드

Generating Representative Samples for Few-Shot Classification

https://github.com/cvxgrp/joint-lrsm

공식 코드

Joint Graph Learning and Model Fitting in Laplacian Regularized Stratified Models

https://github.com/cyang-cityu/MetaCorrection

공식 코드

MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation

https://github.com/cyvius96/few-shot-meta-baseline

공식 코드

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

https://github.com/czifan/raml

공식 코드

Region-Aware Metric Learning for Open World Semantic Segmentation via Meta-Channel Aggregation

https://github.com/czq142857/BAE-NET

공식 코드

BAE-NET: Branched Autoencoder for Shape Co-Segmentation

https://github.com/czx-li/DP2O

공식 코드

Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Generation for Few-shot Learning

https://github.com/czyssrs/Few-Shot-NLG

공식 코드

Few-Shot NLG with Pre-Trained Language Model

https://github.com/dahyun-kang/ifsl

공식 코드

Integrative Few-Shot Learning for Classification and Segmentation

https://github.com/dahyun-kang/renet

공식 코드

Relational Embedding for Few-Shot Classification

https://github.com/daizhongxiang/meta-bo

공식 코드

On Provably Robust Meta-Bayesian Optimization

https://github.com/dali92002/htrbymatching

공식 코드

Few Shots Are All You Need: A Progressive Few Shot Learning Approach for Low Resource Handwritten Text Recognition

https://github.com/damedollaforthree/nrml

공식 코드

Neural Routing in Meta Learning

https://github.com/dammsi/AnomalyDINO

공식 코드

AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2

https://github.com/damo-di-ml/one_fits_all

공식 코드

One Fits All:Power General Time Series Analysis by Pretrained LM

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