Papers with Code 아카이브
Meta-Learning
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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/wenhui0206/MeTSK
공식 코드Generalizable Representation Learning for fMRI-based Neurological Disorder Identification
https://github.com/wentaotan/interleaved-learning
공식 코드Style Interleaved Learning for Generalizable Person Re-identification
https://github.com/wesselb/NeuralProcesses.jl
공식 코드Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes
https://github.com/whaleloops/clinicalmamba
공식 코드ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical Notes
https://github.com/whileherham/imr-hsnet
공식 코드Iterative Few-shot Semantic Segmentation from Image Label Text
https://github.com/whut-yirong/espt
공식 코드ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot Learning
https://github.com/wilburdoz/meta_learning_inductive_bias
공식 코드Meta-Learning the Inductive Biases of Simple Neural Circuits
https://github.com/winycg/MCL-OKD
공식 코드Multi-view Contrastive Learning for Online Knowledge Distillation
https://github.com/winycg/mcl
공식 코드Mutual Contrastive Learning for Visual Representation Learning
https://github.com/wjdghks950/discern-and-answer
공식 코드Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noise
https://github.com/wjjll/metassa
공식 코드Exploring Frequencies via Feature Mixing and Meta-Learning for Improving Adversarial Transferability
https://github.com/wltjr1007/FDEN
공식 코드A Plug-in Method for Representation Factorization in Connectionist Models
https://github.com/worldeditors/evolvingplasticann
공식 코드Evolving Decomposed Plasticity Rules for Information-Bottlenecked Meta-Learning
https://github.com/worldeditors/evolvingprnn
공식 코드Evolving Decomposed Plasticity Rules for Information-Bottlenecked Meta-Learning
https://github.com/wronnyhuang/metapoison
공식 코드MetaPoison: Practical General-purpose Clean-label Data Poisoning
https://github.com/ws-jiang/metaprompter_public
공식 코드Effective Structured Prompting by Meta-Learning and Representative Verbalizer
https://github.com/wshi83/ehragent
공식 코드https://github.com/wssun/promptcs
공식 코드https://github.com/wut-ai/hypersound
공식 코드Hypernetworks build Implicit Neural Representations of Sounds
https://github.com/wutong8023/AAAI-CML
공식 코드Curriculum-Meta Learning for Order-Robust Continual Relation Extraction
https://github.com/wutong8023/AAAI_CML
공식 코드Curriculum-Meta Learning for Order-Robust Continual Relation Extraction
https://github.com/wxie2013/gdfree-supervised-snn-mnist
공식 코드https://github.com/wxyi057/metat2
공식 코드T2-Only Prostate Cancer Prediction by Meta-Learning from Bi-Parametric MR Imaging
https://github.com/wy1iu/NSL
공식 코드https://github.com/wyharveychen/CloserLookFewShot
공식 코드https://github.com/wyjeong/tans
공식 코드Task-Adaptive Neural Network Search with Meta-Contrastive Learning
https://github.com/wyy0206/THzVR
공식 코드Meta-Reinforcement Learning for Reliable Communication in THz/VLC Wireless VR Networks
https://github.com/xiagroup/mf_mlam
공식 코드Meta-learning based Alternating Minimization Algorithm for Non-convex Optimization
https://github.com/xiangyu8/PT-MAP-sf
공식 코드Few-Shot Learning by Integrating Spatial and Frequency Representation
https://github.com/xianyongqin/few-shot-video-classification
공식 코드Generalized Few-Shot Video Classification with Video Retrieval and Feature Generation
https://github.com/xiaomengyc/Few-Shot-Semantic-Segmentation-Papers
공식 코드https://github.com/xiaomengyc/SG-One
공식 코드SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation
https://github.com/xiaoshuihuang/epcl
공식 코드EPCL: Frozen CLIP Transformer is An Efficient Point Cloud Encoder
https://github.com/xin-wang18/MISE
공식 코드MISE: Meta-knowledge Inheritance for Social Media-Based Stressor Estimation
https://github.com/xingpingdong/pl-cfe
공식 코드Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.