https://github.com/MoritzTaylor/maml-rl-tf2
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Papers with Code 아카이브
Few-Shot Learning
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Few-Shot Learning is an example of meta-learning, where a learner is trained on several related tasks, during the meta-training phase, so that it can generalize well to unseen (but related) tasks with just few examples, during the meta-testing phase. An effective approach to the Few-Shot Learning problem is to learn a common representation for various tasks and train task specific classifiers on top of this representation.
Source: Penalty Method for Inversion-Free Deep Bilevel Optimization
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Mushfequr-Rahman/Omniglot_baselines
https://github.com/Muzammal-Naseer/Intriguing-Properties-of-Vision-Transformers
https://github.com/NVIDIA/NeMo-Curator
https://github.com/NilsHellwig/llm-prompting-asqp
https://github.com/Octavio-Pappalardo/MAML_for_RL_pytorch
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Open-Debin/MetaQDA_Pub
Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/few_shot/efl
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/gpt-3
https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/maml
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Papirapi/Few_shot-learning-for-Object-Detection
https://github.com/ProKil/FewRel
https://github.com/RUCAIBox/LLMBox
https://github.com/RajeevReddyIlavala/Low-Shot-Learning-Learning
https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection
https://github.com/RogerQi/pascal-5i
https://github.com/RossNordby/SoftPromptsForEvaluation
https://github.com/Runist/torch_maml
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Ryan0v0/Google_ML_Camp
Artistic Glyph Image Synthesis via One-Stage Few-Shot Learning
https://github.com/RyanKim17920/DeepMoE
https://github.com/SAP-samples/acl2022-self-contrastive-decorrelation
miCSE: Mutual Information Contrastive Learning for Low-shot Sentence Embeddings
https://github.com/Samyu0304/thought-propagation
https://github.com/Savannah120/alignment-handbook-PoFT
https://github.com/SeonbeomKim/TensorFlow-Matching_Networks_for_One_Shot_Learning
https://github.com/Seth-Park/MultimodalExplanations
https://github.com/ShapeAI/OneShotLearning-with-Siamese-
Generalizing from a Few Examples: A Survey on Few-Shot Learning
https://github.com/ShapeAI/OneShotLearning-with-Siamese-character_classification
Generalizing from a Few Examples: A Survey on Few-Shot Learning
https://github.com/Shawn-Inspur/Yuan-1.0
Yuan 1.0: Large-Scale Pre-trained Language Model in Zero-Shot and Few-Shot Learning
https://github.com/ShuvenduRoy/FER_TL_PipelineTraining
Consistency-guided Prompt Learning for Vision-Language Models
https://github.com/SilverQ/NER_Final
Few-shot Learning for Named Entity Recognition in Medical Text
https://github.com/SilverQ/NamedEntityRecognition
Few-shot Learning for Named Entity Recognition in Medical Text
https://github.com/SinghJasdeep/Projecting-Conflicting-Gradients
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Soikonomou/albert_final
Matching the Blanks: Distributional Similarity for Relation Learning
https://github.com/Soikonomou/albert_final_infer12
Matching the Blanks: Distributional Similarity for Relation Learning
https://github.com/Soikonomou/albert_final_infer8
Matching the Blanks: Distributional Similarity for Relation Learning
https://github.com/Soikonomou/bert_new
Matching the Blanks: Distributional Similarity for Relation Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.