https://github.com/shiyuanh/tane
공식 코드Task-Adaptive Negative Envision for Few-Shot Open-Set Recognition
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/shlee625/cxgrad
공식 코드https://github.com/shmsw25/Channel-LM-Prompting
공식 코드Noisy Channel Language Model Prompting for Few-Shot Text Classification
https://github.com/shmulvad/zero-for-ner
공식 코드Zero-Shot Learning in Named-Entity Recognition with External Knowledge
https://github.com/showlab/sct
공식 코드SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient Channels
https://github.com/shruti-jadon/Covid-19-Detection
공식 코드COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach
https://github.com/shruti-jadon/Hands-on-One-Shot-Learning
공식 코드An Overview of Deep Learning Architectures in Few-Shot Learning Domain
https://github.com/shruti-jadon/PlantsDiseaseDetection
공식 코드SSM-Net for Plants Disease Identification in Low Data Regime
https://github.com/shruti-jadon/Siamese-Network-for-One-shot-Learning
공식 코드Improving Siamese Networks for One Shot Learning using Kernel Based Activation functions
https://github.com/shuailyu0110/sql-o1
공식 코드SQL-o1: Automatic Text-to-SQL Program Synthesis via Self-Reward Heuristic Search
https://github.com/shubha07m/MetaGreen-Meta-Learning-Inspired-Transformer-Selection
공식 코드MetaGreen: Meta-Learning Inspired Transformer Selection for Green Semantic Communication
https://github.com/shuguoj/hsi-classification
공식 코드A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samples
https://github.com/shuvenduroy/coprompt
공식 코드Consistency-guided Prompt Learning for Vision-Language Models
https://github.com/shwangtangjun/Diff-ResNet
공식 코드Diffusion Mechanism in Residual Neural Network: Theory and Applications
https://github.com/siddhantjain/PointCloudAnnotationTool
공식 코드Few-Shot Point Cloud Region Annotation with Human in the Loop
https://github.com/silverbottlep/meta_curvature
공식 코드https://github.com/simonguiroy/abe
공식 코드Improving Meta-Learning Generalization with Activation-Based Early-Stopping
https://github.com/sinahmr/diam
공식 코드A Strong Baseline for Generalized Few-Shot Semantic Segmentation
https://github.com/sirkosophia/dip
공식 코드DIP: Unsupervised Dense In-Context Post-training of Visual Representations
https://github.com/sisl/MPHRL
공식 코드Model Primitive Hierarchical Lifelong Reinforcement Learning
https://github.com/siyandong/src
공식 코드Visual Localization via Few-Shot Scene Region Classification
https://github.com/skingorz/ggiu
공식 코드https://github.com/skoohy/gpt-pinn
공식 코드https://github.com/slei109/tart
공식 코드TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation
https://github.com/smappnyu/dascl
공식 코드https://github.com/smartyfh/dst-metaassist
공식 코드MetaASSIST: Robust Dialogue State Tracking with Meta Learning
https://github.com/smonsays/contrastive-meta-learning
공식 코드https://github.com/smonsays/modular-hyperteacher
공식 코드Discovering modular solutions that generalize compositionally
https://github.com/smufang/fewshotgraph
공식 코드A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learning
https://github.com/snap-stanford/caml
공식 코드https://github.com/snap-stanford/comet
공식 코드https://github.com/snap-stanford/csr
공식 코드Few-shot Relational Reasoning via Connection Subgraph Pretraining
https://github.com/snap-stanford/roland
공식 코드https://github.com/social-ai-studio/mod_hate
공식 코드https://github.com/sohiniroych/Paralllel-ESN-For-Image-Quality
공식 코드Few Shot Learning Framework to Reduce Inter-observer Variability in Medical Images
https://github.com/solarlee/fewsar
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.