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/google/example_extrapolation
공식 코드https://github.com/google/jaxopt
공식 코드https://github.com/google/neural-tangents
공식 코드https://github.com/gortizji/inr_dictionaries
공식 코드A Structured Dictionary Perspective on Implicit Neural Representations
https://github.com/graph-com/meta_co
공식 코드Unsupervised Learning for Combinatorial Optimization Needs Meta-Learning
https://github.com/graphmolab/graph2token
공식 코드https://github.com/grasp-lyrl/picture_of_space_of_tasks
공식 코드https://github.com/griffbr/tfod
공식 코드https://github.com/gtzheng/SPUME
공식 코드Spuriousness-Aware Meta-Learning for Learning Robust Classifiers
https://github.com/gtzheng/fewstab
공식 코드https://github.com/guangxinghan/meta-faster-r-cnn
공식 코드Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment
https://github.com/guanyu-lin/inverselearning
공식 코드Inverse Learning with Extremely Sparse Feedback for Recommendation
https://github.com/guliisgreat/vdpg
공식 코드Adapting to Distribution Shift by Visual Domain Prompt Generation
https://github.com/guodongqi/cmvae
공식 코드https://github.com/guoqianyu-alberta/rankdnn
공식 코드https://github.com/guoxuxu/LOANT
공식 코드Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection
https://github.com/guoyue-cynthia/learning-ode-integrators
공식 코드Personalized Algorithm Generation: A Case Study in Learning ODE Integrators
https://github.com/guxiao0822/lt-ds
공식 코드Tackling Long-Tailed Category Distribution Under Domain Shifts
https://github.com/guybuk/ANCOR
공식 코드Fine-grained Angular Contrastive Learning with Coarse Labels
https://github.com/gy20073/compact_bilinear_pooling
공식 코드https://github.com/ha-lins/GEML-MDG
공식 코드Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation
https://github.com/haamoon/finding_common_object
공식 코드Learning to Find Common Objects Across Few Image Collections
https://github.com/hadijomaa/dataset2vec
공식 코드https://github.com/hadjisma/VideoAlignment
공식 코드Representation Learning via Global Temporal Alignment and Cycle-Consistency
https://github.com/haebeom-lee/l2b
공식 코드Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks
https://github.com/haidongz-usc/CaesarNeRF
공식 코드CaesarNeRF: Calibrated Semantic Representation for Few-shot Generalizable Neural Rendering
https://github.com/halo2tang/code-for-lwfm-spm
공식 코드Layer-Wise Feature Metric of Semantic-Pixel Matching for Few-Shot Learning
https://github.com/hamedhemati/omsi
공식 코드Sample Weight Estimation Using Meta-Updates for Online Continual Learning
https://github.com/hamilton-health-sciences/ml4h-traq
공식 코드Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?
https://github.com/han-jia/lastshot
공식 코드https://github.com/han-jia/unicorn-maml
공식 코드https://github.com/hanbyel0105/CamDistHumanPose3D
공식 코드Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-Learning
https://github.com/hang53/mega
공식 코드Bootstrapping Informative Graph Augmentation via A Meta Learning Approach
https://github.com/hanjiale/hcrp
공식 코드https://github.com/hanliu95/metammf
공식 코드Dynamic Multimodal Fusion via Meta-Learning Towards Micro-Video Recommendation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.