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/zdsy/proto-MAML

공식 코드

Episodic fine-tuning prototypical networks for optimization-based few-shot learning: Application to audio classification

https://github.com/zequnl/CMAML

공식 코드

Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks

https://github.com/zerojumpline/jcsaugment

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Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation

https://github.com/zerui981105/augfl

공식 코드

AugFL: Augmenting Federated Learning with Pretrained Models

https://github.com/zgjiangtoby/FND_fewshot

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Cross-Modal Augmentation for Few-Shot Multimodal Fake News Detection

https://github.com/zhang-lingyun/ConceptReplacer

공식 코드

Concept Replacer: Replacing Sensitive Concepts in Diffusion Models via Precision Localization

https://github.com/zhangbq-research/Prototype_Completion_for_FSL

공식 코드

Prototype Completion with Primitive Knowledge for Few-Shot Learning

https://github.com/zhangbq-research/metanode

공식 코드

MetaNODE: Prototype Optimization as a Neural ODE for Few-Shot Learning

https://github.com/zhangce01/dualadapter

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Enhancing Vision-Language Few-Shot Adaptation with Negative Learning

https://github.com/zhangce01/simnl

공식 코드

Enhancing Vision-Language Few-Shot Adaptation with Negative Learning

https://github.com/zhangdoudou123/semfew

공식 코드

Simple Semantic-Aided Few-Shot Learning

https://github.com/zhangtao2022/dsfn

공식 코드

Kernel Relative-prototype Spectral Filtering for Few-shot Learning

https://github.com/zhangxueting/DCN

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RelationNet2: Deep Comparison Columns for Few-Shot Learning

https://github.com/zhangyilang/ibaml

공식 코드

Scalable Bayesian Meta-Learning through Generalized Implicit Gradients

https://github.com/zhangyilang/metamirrordescent

공식 코드

Meta-Learning with Versatile Loss Geometries for Fast Adaptation Using Mirror Descent

https://github.com/zhangzilongc/sofs

공식 코드

Small Object Few-shot Segmentation for Vision-based Industrial Inspection

https://github.com/zhangzizhen/ml-dam

공식 코드

Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization Problems

https://github.com/zhaohengyuan1/sct

공식 코드

SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient Channels

https://github.com/zhen-tan-dmml/gfcil

공식 코드

Graph Few-shot Class-incremental Learning

https://github.com/zhen-tan-dmml/tlp-fsnc

공식 코드

Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification

https://github.com/zhengli97/dm-kd

공식 코드

Is Synthetic Data From Diffusion Models Ready for Knowledge Distillation?

https://github.com/zhengxiangshi/dept

공식 코드

DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning

https://github.com/zhengxiangshi/powerfulpromptft

공식 코드

Rethink the Effectiveness of Text Data Augmentation: An Empirical Analysis

https://github.com/zheyangh/ocgnet

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Object-level Cross-view Geo-localization with Location Enhancement and Multi-Head Cross Attention

https://github.com/zhichunguo/Meta-MGNN

공식 코드

Few-Shot Graph Learning for Molecular Property Prediction

https://github.com/zhiheLu/CWT-for-FSS

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Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer

https://github.com/zhimin-c/multiview-mae

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Point Cloud Self-supervised Learning via 3D to Multi-view Masked Autoencoder

https://github.com/zhipeng-wei/EmojiAttack

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Emoji Attack: A Method for Misleading Judge LLMs in Safety Risk Detection

https://github.com/zhongwanjun/proqa

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ProQA: Structural Prompt-based Pre-training for Unified Question Answering

https://github.com/zhoudw-zdw/TPAMI-Limit

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Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks

https://github.com/zhoudw-zdw/tpami-leadnet

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Contextualizing Meta-Learning via Learning to Decompose

https://github.com/zhouj8553/flipda

공식 코드

FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning

https://github.com/zhoulong0/unem-transductive

공식 코드

UNEM: UNrolled Generalized EM for Transductive Few-Shot Learning

https://github.com/zhuang-li/few-shot-semantic-parsing

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Few-Shot Semantic Parsing for New Predicates

https://github.com/zhuang-li/vae-dprior

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Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation

https://github.com/zhuhsingyuu/ssp

공식 코드

Selective Vision-Language Subspace Projection for Few-shot CLIP

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