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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/lehduong/poodle

공식 코드

POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples

https://github.com/leileicao/protoformer

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Prototype as Query for Few Shot Semantic Segmentation

https://github.com/lenkatetkova/convexity-of-representations

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On convex decision regions in deep network representations

https://github.com/leobertolazzi/mind

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A MIND for Reasoning: Meta-learning for In-context Deduction

https://github.com/leopard-ai/betty

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Betty: An Automatic Differentiation Library for Multilevel Optimization

https://github.com/lephuoccat/TAS-few-shot

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Task Affinity with Maximum Bipartite Matching in Few-Shot Learning

https://github.com/lexili24/NLUProject

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Meta-Learning for Natural Language Understanding under Continual Learning Framework

https://github.com/leymir/hyperbolic-image-embeddings

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Hyperbolic Image Embeddings

https://github.com/lezhangleonard/E-QUARTIC

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E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized Learning

https://github.com/lgnyu/vitseg

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Applying ViT in Generalized Few-shot Semantic Segmentation

https://github.com/lhtie/bio-domain-transfer

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Named Entity Recognition Under Domain Shift via Metric Learning for Life Sciences

https://github.com/lian-xiao/biobridge

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Inductive-Associative Meta-learning Pipeline with Human Cognitive Patterns for Unseen Drug-Target Interaction Prediction

https://github.com/liangjunfeng/srwgan

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Bias-Eliminated Semantic Refinement for Any-Shot Learning

https://github.com/liatb282/metaxlr

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MetaXLR -- Mixed Language Meta Representation Transformation for Low-resource Cross-lingual Learning based on Multi-Armed Bandit

https://github.com/likesiwell/LGM-Net

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LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning

https://github.com/lingl1024/causalfewshot

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Revisiting Few-Shot Learning from a Causal Perspective

https://github.com/linh-gist/gazeestimationtx2

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Resolving Camera Position for a Practical Application of Gaze Estimation on Edge Devices

https://github.com/linhanwang/sccnet

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Self-Correlation and Cross-Correlation Learning for Few-Shot Remote Sensing Image Semantic Segmentation

https://github.com/linjx-ustc1106/MT-GAN-PyTorch

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Learning to Transfer: Unsupervised Meta Domain Translation

https://github.com/linusericsson/ssl-transfer

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How Well Do Self-Supervised Models Transfer?

https://github.com/linzhiqiu/cross_modal_adaptation

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Multimodality Helps Unimodality: Cross-Modal Few-Shot Learning with Multimodal Models

https://github.com/lionellee9089/metamask

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MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning

https://github.com/lionellee9089/metaug

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MetAug: Contrastive Learning via Meta Feature Augmentation

https://github.com/lisha-chen/Bayesian-MAML-vs-MAML

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Is Bayesian Model-Agnostic Meta Learning Better than Model-Agnostic Meta Learning, Provably?

https://github.com/lishuo1001/ldc

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Logits DeConfusion with CLIP for Few-Shot Learning

https://github.com/lisuyi/pbadv_czsl

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Contextual Interaction via Primitive-based Adversarial Training For Compositional Zero-shot Learning

https://github.com/liulu112601/Gated-Propagation-Net

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Learning to Propagate for Graph Meta-Learning

https://github.com/liulu112601/MahiNet

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Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy

https://github.com/liulu112601/PPN

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Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph

https://github.com/liulu112601/Prototype-Propagation-Networks

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Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph

https://github.com/liuquande/SAML

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Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains

https://github.com/liuran998/RelAdapter

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Context-Aware Adapter Tuning for Few-Shot Relation Learning in Knowledge Graphs

https://github.com/liutongkun/gfds

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Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models

https://github.com/liuweide01/HBNC

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Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation

https://github.com/liuweijie19980216/DRL-for-FSOD

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Dynamic Relevance Learning for Few-Shot Object Detection

https://github.com/liuyixin-louis/metacloak

공식 코드

MetaCloak: Preventing Unauthorized Subject-driven Text-to-image Diffusion-based Synthesis via Meta-learning

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