Papers with Code 아카이브
Meta-Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/nathanielsimard/improving-fs-ssl
공식 코드Improving Few-Shot Learning with Auxiliary Self-Supervised Pretext Tasks
https://github.com/naver-ai/carecall-corpus
공식 코드Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models
https://github.com/naver/biobert-pretrained
공식 코드BioBERT: a pre-trained biomedical language representation model for biomedical text mining
https://github.com/nayeemrizve/invariance-equivariance
공식 코드Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning
https://github.com/neuraloperator/coda-no
공식 코드Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs
https://github.com/neuroevobench/neuroevobench
공식 코드NeuroEvoBench: Benchmarking Evolutionary Optimizers for Deep Learning Applications
https://github.com/nevermorelch/ccg
공식 코드Consistency of Compositional Generalization across Multiple Levels
https://github.com/ngandh/F2LCough
공식 코드Federated Few-shot Learning for Cough Classification with Edge Devices
https://github.com/ngessert/isic2018
공식 코드Skin Lesion Diagnosis using Ensembles, Unscaled Multi-Crop Evaluation and Loss Weighting
https://github.com/ngl567/GANA-FewShotKGC
공식 코드https://github.com/nhhoang96/Semantic_Matching
공식 코드Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection
https://github.com/nicola-decao/KnowledgeEditor
공식 코드https://github.com/nicolas-lair/AidMe
공식 코드User-in-the-loop Adaptive Intent Detection for Instructable Digital Assistant
https://github.com/nicpopovic/fredo
공식 코드https://github.com/niejiahao1998/ifa
공식 코드Cross-Domain Few-Shot Segmentation via Iterative Support-Query Correspondence Mining
https://github.com/nielsschluesener/fast-learning-hand-gesture-recognition
공식 코드Fast Learning of Dynamic Hand Gesture Recognition with Few-Shot Learning Models
https://github.com/nii-yamagishilab/ssl_vits_df
공식 코드Exploring Self-Supervised Vision Transformers for Deepfake Detection: A Comparative Analysis
https://github.com/nileshtrip/MTL
공식 코드https://github.com/ningyuxu/structural_concepts_correspondence
공식 코드https://github.com/nisarahmedrana/arabicmlec
공식 코드https://github.com/nitaytech/domainrobustness
공식 코드https://github.com/nitaytech/domainrobustnessdata
공식 코드https://github.com/njuhtc/LEDG
공식 코드https://github.com/njulus/ST
공식 코드https://github.com/njulus/gkd
공식 코드Generalized Knowledge Distillation via Relationship Matching
https://github.com/nle18/coref-llms
공식 코드https://github.com/nmank/FD
공식 코드https://github.com/nmi-lab/snn_maml
공식 코드Emulating Brain-like Rapid Learning in Neuromorphic Edge Computing
https://github.com/nobody-1617/deta
공식 코드https://github.com/nobodymx/resilient-swarm-communications-with-meta-graph-convolutional-networks
공식 코드Resilient UAV Swarm Communications with Graph Convolutional Neural Network
https://github.com/noycohen100/MARCO-GE
공식 코드Automatic selection of clustering algorithms using supervised graph embedding
https://github.com/nraghuraman/bongard-context
공식 코드https://github.com/nsaunshi/meta_tr_val_split
공식 코드https://github.com/numenta/htmpapers/tree/master/bmvc/grid_cell_path_integration_for_movement_based_visual_object_recognition
공식 코드Grid Cell Path Integration For Movement-Based Visual Object Recognition
https://github.com/nupurkmr9/S2M2_fewshot
공식 코드Charting the Right Manifold: Manifold Mixup for Few-shot Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.