Papers with Code 아카이브

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/chrisyxue/RCN_for_Interpretable_few_shot

공식 코드

Region Comparison Network for Interpretable Few-shot Image Classification

https://github.com/chu-data-lab/GOGGLES

공식 코드

GOGGLES: Automatic Image Labeling with Affinity Coding

https://github.com/chunbolang/BAM

공식 코드

Learning What Not to Segment: A New Perspective on Few-Shot Segmentation

https://github.com/chunbolang/DCP

공식 코드

Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentation

https://github.com/chuxiaokai/baidu_ultr_dataset

공식 코드

A Large Scale Search Dataset for Unbiased Learning to Rank

https://github.com/chuxuzhang/AAAI2020_FSRL

공식 코드

Few-Shot Knowledge Graph Completion

https://github.com/ciao-seohyeon/l2e

공식 코드

Learning to Explore for Stochastic Gradient MCMC

https://github.com/cjfarmer/trd_fsl

공식 코드

Pre-trained Token-replaced Detection Model as Few-shot Learner

https://github.com/cl-victor1/exalt_2024_bcsz

공식 코드

TEII: Think, Explain, Interact and Iterate with Large Language Models to Solve Cross-lingual Emotion Detection

https://github.com/claws-lab/few-shot-adversarial-robustness

공식 코드

Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language Understanding

https://github.com/clcl-geneva/blm-snfdisentangling

공식 코드

Grammatical information in BERT sentence embeddings as two-dimensional arrays

https://github.com/cleardusk/MFR

공식 코드

Learning Meta Face Recognition in Unseen Domains

https://github.com/cleopatra-itn/sentimentanalyserslhrnews

공식 코드

Multi-task Learning for Cross-Lingual Sentiment Analysis

https://github.com/cliffbb/oem-fewshot-challenge

공식 코드

Generalized Few-Shot Semantic Segmentation in Remote Sensing: Challenge and Benchmark

https://github.com/clovaai/wsolevaluation

공식 코드

Evaluating Weakly Supervised Object Localization Methods Right

https://github.com/clp-research/language-models-multimodal-tasks

공식 코드

Images in Language Space: Exploring the Suitability of Large Language Models for Vision & Language Tasks

https://github.com/clulab/releases

공식 코드

From Examples to Rules: Neural Guided Rule Synthesis for Information Extraction

https://github.com/cmu-plus/lak2025-inequity

공식 코드

Do Tutors Learn from Equity Training and Can Generative AI Assess It?

https://github.com/cnewbie007/DADG

공식 코드

Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain Validation

https://github.com/cnguyen10/few_shot_meta_learning

공식 코드

Uncertainty in Model-Agnostic Meta-Learning using Variational Inference

https://github.com/cnguyen10/probabilistic_task_modelling

공식 코드

Probabilistic task modelling for meta-learning

https://github.com/cnguyen10/similarity_classification_tasks

공식 코드

Similarity of Classification Tasks

https://github.com/coderpat/learning-scaffold

공식 코드

Learning to Scaffold: Optimizing Model Explanations for Teaching

https://github.com/cods-gcs/kgpip-public

공식 코드

A Scalable AutoML Approach Based on Graph Neural Networks

https://github.com/cofly2014/tsa-mlt

공식 코드

Task-Specific Alignment and Multiple Level Transformer for Few-Shot Action Recognition

https://github.com/cogentlabs/apl

공식 코드

Adaptive Posterior Learning: few-shot learning with a surprise-based memory module

https://github.com/cognibit/Text-Normalization-Demo

공식 코드

Text normalization using memory augmented neural networks

https://github.com/cognizant-ai-labs/autoinit

공식 코드

AutoInit: Analytic Signal-Preserving Weight Initialization for Neural Networks

https://github.com/cogsys-tudelft/chameleon

공식 코드

Chameleon: A MatMul-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data

https://github.com/colinzhaoust/intrinsic_fewshot_hardness

공식 코드

On Measuring the Intrinsic Few-Shot Hardness of Datasets

https://github.com/complex-ai-lab/ncc

공식 코드

Neural Conformal Control for Time Series Forecasting

https://github.com/composable-models/llm_multiagent_debate

공식 코드

Improving Factuality and Reasoning in Language Models through Multiagent Debate

https://github.com/computer-vision-in-the-wild/cvinw_readings

공식 코드

Vision-Language Pre-training: Basics, Recent Advances, and Future Trends

https://github.com/cooelf/OneshotQA

공식 코드

One-shot Learning for Question-Answering in Gaokao History Challenge

https://github.com/copenlu/X-MAML

공식 코드

Zero-Shot Cross-Lingual Transfer with Meta Learning

https://github.com/copenlu/scientific-exaggeration-detection

공식 코드

Semi-Supervised Exaggeration Detection of Health Science Press Releases

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