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/ilyassmoummad/dcase23_task5_scl

공식 코드

Pretraining Representations for Bioacoustic Few-shot Detection using Supervised Contrastive Learning

https://github.com/ilyassmoummad/rcl_fs_bsed

공식 코드

Regularized Contrastive Pre-training for Few-shot Bioacoustic Sound Detection

https://github.com/ilyassmoummad/ssl4birdsounds

공식 코드

Self-Supervised Learning for Few-Shot Bird Sound Classification

https://github.com/imperial-qore/tranad

공식 코드

TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data

https://github.com/imtiazziko/LaplacianShot

공식 코드

Laplacian Regularized Few-Shot Learning

https://github.com/imtiazziko/SLK-few-shot

공식 코드

Transductive Few-Shot Learning: Clustering is All You Need?

https://github.com/indy-lab/ProtoTransfer

공식 코드

Self-Supervised Prototypical Transfer Learning for Few-Shot Classification

https://github.com/infinity7428/hANMCL

공식 코드

Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learning

https://github.com/informsjoc/2023.0281

공식 코드

Multi-Objective Linear Ensembles for Robust and Sparse Training of Few-Bit Neural Networks

https://github.com/instadeepai/metalic

공식 코드

Metalic: Meta-Learning In-Context with Protein Language Models

https://github.com/intel-isl/MetaLearningTradeoffs

공식 코드

Modeling and Optimization Trade-off in Meta-learning

https://github.com/interpretml/llm-tabular-memorization-checker

공식 코드

Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models

https://github.com/irom-lab/PAC-BUS

공식 코드

Generalization Bounds for Meta-Learning via PAC-Bayes and Uniform Stability

https://github.com/isakfalk/mela

공식 코드

Robust Meta-Representation Learning via Global Label Inference and Classification

https://github.com/isekai-portal/Link-Context-Learning

공식 코드

Link-Context Learning for Multimodal LLMs

https://github.com/ishan00/meta-learning-for-multi-task-multilingual

공식 코드

Meta-Learning for Effective Multi-task and Multilingual Modelling

https://github.com/isl-org/lang-seg

공식 코드

Language-driven Semantic Segmentation

https://github.com/islam-nassar/lava

공식 코드

LAVA: Label-efficient Visual Learning and Adaptation

https://github.com/ispamm/mate

공식 코드

A Meta-Learning Approach for Training Explainable Graph Neural Networks

https://github.com/istarjun/TapNet

공식 코드

TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning

https://github.com/iupui-soic/radiology_fsl

공식 코드

Few-Shot Transfer Learning to improve Chest X-Ray pathology detection using limited triplets

https://github.com/ivonajdenkoska/multimodal-meta-learn

공식 코드

Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot Learning

https://github.com/ivul-kaust/mole

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MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs

https://github.com/jacksonfurrier/isbi_2024_code

공식 코드

Continuous max-flow augmentation of self-supervised few-shot learning on SPECT left ventricles

https://github.com/jacooba/hyper

공식 코드

Recurrent Hypernetworks are Surprisingly Strong in Meta-RL

https://github.com/jaeho-lee/MetaSparseINR

공식 코드

Meta-Learning Sparse Implicit Neural Representations

https://github.com/jaehyun513/stunt

공식 코드

STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables

https://github.com/jaeill/CVPR23-VNE

공식 코드

VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution

https://github.com/jahawn-wen/camel-reid

공식 코드

CAMeL: Cross-modality Adaptive Meta-Learning for Text-based Person Retrieval

https://github.com/jakesnell/prototypical-networks

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Prototypical Networks for Few-shot Learning

https://github.com/jamqd/Group-Preference-Optimization

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Group Preference Optimization: Few-Shot Alignment of Large Language Models

https://github.com/jaraxxus-me/airdet

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AirDet: Few-Shot Detection without Fine-tuning for Autonomous Exploration

https://github.com/jarch-ma/AFANet

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AFANet: Adaptive Frequency-Aware Network for Weakly-Supervised Few-Shot Semantic Segmentation

https://github.com/jarucezh/cldfd

공식 코드

Cross-Level Distillation and Feature Denoising for Cross-Domain Few-Shot Classification

https://github.com/jarvis73/fptrans

공식 코드

Feature-Proxy Transformer for Few-Shot Segmentation

https://github.com/jaryP/CentroidsMatching

공식 코드

Centroids Matching: an efficient Continual Learning approach operating in the embedding space

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