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/ArchitParnami/Few-Shot-Privacy

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Privacy Enhancement for Cloud-Based Few-Shot Learning

https://github.com/ArghyaPal/Zero-shot-task-transfer

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Zero-Shot Task Transfer

https://github.com/ArnoutDevos/RegressionNet

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Regression Networks for Meta-Learning Few-Shot Classification

https://github.com/Arvid-pku/Godel_Agent

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Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement

https://github.com/AshishMittal/RMLIntent

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Representation based meta-learning for few-shot spoken intent recognition

https://github.com/AtmaHou/FewShotTagging

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Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network

https://github.com/AtmaHou/MetaDialog

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FewJoint: A Few-shot Learning Benchmark for Joint Language Understanding

https://github.com/AutodeskAILab/UVStyle-Net

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UVStyle-Net: Unsupervised Few-shot Learning of 3D Style Similarity Measure for B-Reps

https://github.com/AuvaLab/itext2kg

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iText2KG: Incremental Knowledge Graphs Construction Using Large Language Models

https://github.com/AyanKumarBhunia/Deep-One-Shot-Logo-Retrieval

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A Deep One-Shot Network for Query-based Logo Retrieval

https://github.com/Azizimj/Meta-SRM

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Meta-Learning for Simple Regret Minimization

https://github.com/BDBC-KG-NLP/AAAI2022_ContrastNet

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ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification

https://github.com/BIT-DA/MetaSAug

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MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition

https://github.com/Babakbehkamkia/GPT-Stance-Detection

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Zero-Shot Stance Detection using Contextual Data Generation with LLMs

https://github.com/BaiShuanghao/CDFSC-MLP

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Improving Cross-domain Few-shot Classification with Multilayer Perceptron

https://github.com/Basvanstein/doe2vec

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DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis

https://github.com/BatsResearch/efsl

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Extended Few-Shot Learning: Exploiting Existing Resources for Novel Tasks

https://github.com/BayesWatch/deep-kernel-transfer

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Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels

https://github.com/BeautyOfWeb/AffinityNet

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AffinityNet: semi-supervised few-shot learning for disease type prediction

https://github.com/Bibkiller/ASR

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Anti-aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation

https://github.com/BinahHu/ADE-FewShot

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Unlocking the Full Potential of Small Data with Diverse Supervision

https://github.com/BorealisAI/meta-tpp

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Meta Temporal Point Processes

https://github.com/Breakend/SocraticSwarm

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Cost Adaptation for Robust Decentralized Swarm Behaviour

https://github.com/CGuangyan-BIT/PointGPT

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PointGPT: Auto-regressively Generative Pre-training from Point Clouds

https://github.com/CLUEbenchmark/FewCLUE

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FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark

https://github.com/Cerenaut/aha

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Unsupervised One-shot Learning of Both Specific Instances and Generalised Classes with a Hippocampal Architecture

https://github.com/Chain123/Meta-HAR

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Meta-HAR: Federated Representation Learning for Human Activity Recognition

https://github.com/CharlieDinh/pFedMe

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Personalized Federated Learning with Moreau Envelopes

https://github.com/ChengtaiCao/Meta-GNN

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Meta-GNN: On Few-shot Node Classification in Graph Meta-learning

https://github.com/ChunyuLiu188/SpectrumFM

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SpectrumFM: A Foundation Model for Intelligent Spectrum Management

https://github.com/Clarifai/few-shot-ctm

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Finding Task-Relevant Features for Few-Shot Learning by Category Traversal

https://github.com/Codelegant92/STC-MAML-PyTorch

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An Investigation of Few-Shot Learning in Spoken Term Classification

https://github.com/CrawlScript/tf_geometric

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Efficient Graph Deep Learning in TensorFlow with tf_geometric

https://github.com/DaphneAntotsiou/MAPS

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Modular Adaptive Policy Selection for Multi-Task Imitation Learning through Task Division

https://github.com/Darthholi/similarity-models

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Learning from similarity and information extraction from structured documents

https://github.com/DataSystemsGroupUT/automl_exploration_vs_exploitation

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AutoML: Exploration v.s. Exploitation

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