https://github.com/vkverma01/Zero-Shot
공식 코드A Simple Exponential Family Framework for Zero-Shot Learning
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/vkverma01/Zero-Shot-Learning
공식 코드A Simple Exponential Family Framework for Zero-Shot Learning
https://github.com/vl2g/VRC
공식 코드https://github.com/vturrisi/disef
공식 코드Diversified in-domain synthesis with efficient fine-tuning for few-shot classification
https://github.com/vuoristo/MMAML
공식 코드Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation
https://github.com/vuoristo/MMAML-Regression
공식 코드https://github.com/vuoristo/meta-gradients
공식 코드An Investigation of the Bias-Variance Tradeoff in Meta-Gradients
https://github.com/vvorakit/Entity-Aspect-Sentiment-Triplet-Extraction
공식 코드Deep Content Understanding Toward Entity and Aspect Target Sentiment Analysis on Foundation Models
https://github.com/w-wu/has_cnf
공식 코드It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density Estimation
https://github.com/walkerworldpeace/free
공식 코드https://github.com/walkerworldpeace/tgr
공식 코드Task Groupings Regularization: Data-Free Meta-Learning with Heterogeneous Pre-trained Models
https://github.com/wangchen1801/fpd
공식 코드Fine-Grained Prototypes Distillation for Few-Shot Object Detection
https://github.com/wangjingyao07/adaptive-sampler
공식 코드https://github.com/wangjingyao07/metacrl
공식 코드https://github.com/wangjingyao07/neuronml
공식 코드https://github.com/wangjingyao07/st-f2m
공식 코드Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition
https://github.com/wangjlgz/MetaTL
공식 코드Sequential Recommendation for Cold-start Users with Meta Transitional Learning
https://github.com/wangkai930418/ERD
공식 코드Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image Recognition
https://github.com/wangkiw/teen
공식 코드Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration
https://github.com/wangpeiyi9979/esd
공식 코드An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling
https://github.com/wangren09/MetaAdv
공식 코드On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning
https://github.com/wangtz19/NetMamba
공식 코드NetMamba: Efficient Network Traffic Classification via Pre-training Unidirectional Mamba
https://github.com/wangxinyilinda/concept-based-demonstration-selection
공식 코드https://github.com/waterhorse1/CMML_pytorch
공식 코드CMML: Contextual Modulation Meta Learning for Cold-Start Recommendation
https://github.com/wayne0908/Active-Learning-Homology
공식 코드Finding the Homology of Decision Boundaries with Active Learning
https://github.com/wbw520/MTUNet
공식 코드Match Them Up: Visually Explainable Few-shot Image Classification
https://github.com/webis-de/ACL-18
공식 코드https://github.com/weiguopian/metatptrans
공식 코드MetaTPTrans: A Meta Learning Approach for Multilingual Code Representation Learning
https://github.com/weitianxin/CLOVER
공식 코드https://github.com/weiwei1206/cml
공식 코드Contrastive Meta Learning with Behavior Multiplicity for Recommendation
https://github.com/weixinxu666/braingfm
공식 코드A Brain Graph Foundation Model: Pre-Training and Prompt-Tuning for Any Atlas and Disorder
https://github.com/wenbinlee/defensivefsl
공식 코드https://github.com/wenbinlee/trip-roma
공식 코드Trip-ROMA: Self-Supervised Learning with Triplets and Random Mappings
https://github.com/wenchen-chen/s4l-fsc
공식 코드Spectral-Spatial Self-Supervised Learning for Few-Shot Hyperspectral Image Classification
https://github.com/wendybaiyunwei/fsl-rectifier-pub
공식 코드FSL-Rectifier: Rectify Outliers in Few-Shot Learning via Test-Time Augmentation
https://github.com/wengbenjue/llms-peft-cook
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.