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/auniquesun/ppt

공식 코드

Parameter-efficient Prompt Learning for 3D Point Cloud Understanding

https://github.com/authors-1901-10912/A-Meta-Transfer-Objective-For-Learning-To-Disentangle-Causal-Mechanisms

공식 코드

A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms

https://github.com/automl/ASKL2.0_experiments

공식 코드

Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning

https://github.com/automl/is_mamba_capable_of_icl

공식 코드

Is Mamba Capable of In-Context Learning?

https://github.com/automl/learna

공식 코드

Learning to Design RNA

https://github.com/automl/transfer-hpo-framework

공식 코드

Practical Transfer Learning for Bayesian Optimization

https://github.com/automl/zero-shot-automl-with-pretrained-models

공식 코드

Zero-Shot AutoML with Pretrained Models

https://github.com/autumn9999/hnps

공식 코드

Episodic Multi-Task Learning with Heterogeneous Neural Processes

https://github.com/avm-debatr/bagganhq

공식 코드

Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN Images

https://github.com/avm-debatr/ganecdotes

공식 코드

Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN Images

https://github.com/avyavkumar/few-shot-learning-notebooks

공식 코드

Learning New Tasks from a Few Examples with Soft-Label Prototypes

https://github.com/awebson/prompt_semantics

공식 코드

Do Prompt-Based Models Really Understand the Meaning of their Prompts?

https://github.com/ayankumarbhunia/adaptive-fgsbir

공식 코드

Adaptive Fine-Grained Sketch-Based Image Retrieval

https://github.com/azizanlab/unlimitd

공식 코드

Uncertainty-Aware Meta-Learning for Multimodal Task Distributions

https://github.com/azoz01/liltab

공식 코드

Rethinking of Encoder-based Warm-start Methods in Hyperparameter Optimization

https://github.com/baaivision/painter

공식 코드

SegGPT: Segmenting Everything In Context

https://github.com/baiksung/ALFA

공식 코드

Meta-Learning with Adaptive Hyperparameters

https://github.com/baiksung/L2F

공식 코드

Learning to Forget for Meta-Learning

https://github.com/baiksung/MeTAL

공식 코드

Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning

https://github.com/baiyimeng/labelcraft

공식 코드

LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting

https://github.com/bapfl/code

공식 코드

You Can Backdoor Personalized Federated Learning

https://github.com/barterer/lp

공식 코드

How Low Can We Go: Trading Memory for Error in Low-Precision Training

https://github.com/basiralab/GmTE-Net

공식 코드

A Few-shot Learning Graph Multi-Trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint

https://github.com/basiralab/meta-reggnn

공식 코드

Meta-RegGNN: Predicting Verbal and Full-Scale Intelligence Scores using Graph Neural Networks and Meta-Learning

https://github.com/basiralab/multigraphgnet

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Predicting Brain Multigraph Population From a Single Graph Template for Boosting One-Shot Classification

https://github.com/basiralab/one-representative-shot-learning

공식 코드

One Representative-Shot Learning Using a Population-Driven Template with Application to Brain Connectivity Classification and Evolution Prediction

https://github.com/bbbdylan/unisiam

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Self-Supervision Can Be a Good Few-Shot Learner

https://github.com/bcmi/Awesome-Weak-Shot-Learning

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Weak Novel Categories without Tears: A Survey on Weak-Shot Learning

https://github.com/bdvllrs/generalization-vision

공식 코드

Does language help generalization in vision models?

https://github.com/beepkh/supmer

공식 코드

Self-supervised Meta-Prompt Learning with Meta-Gradient Regularization for Few-shot Generalization

https://github.com/beierzhu/prompt-align

공식 코드

Prompt-aligned Gradient for Prompt Tuning

https://github.com/berlino/tensor2struct-public

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Meta-Learning to Compositionally Generalize

https://github.com/bethgelab/siamese-mask-rcnn

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One-Shot Instance Segmentation

https://github.com/betray12138/task-representation-shift

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Scrutinize What We Ignore: Reining In Task Representation Shift Of Context-Based Offline Meta Reinforcement Learning

https://github.com/bghojogh/Histopathology-Magnification-Generalization

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Magnification Generalization for Histopathology Image Embedding

https://github.com/bghojogh/Siamese-Network-Histopathology

공식 코드

Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study

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