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/marcdemers/FIGR-8
공식 코드https://github.com/mario-sanz/cicl
공식 코드Corrective In-Context Learning: Evaluating Self-Correction in Large Language Models
https://github.com/mark-sky/kcl
공식 코드The Devil is in the Few Shots: Iterative Visual Knowledge Completion for Few-shot Learning
https://github.com/markpwoodward/active_osl
공식 코드https://github.com/markustyj/fsponer_ecai2024
공식 코드FsPONER: Few-shot Prompt Optimization for Named Entity Recognition in Domain-specific Scenarios
https://github.com/martayang/sega
공식 코드SEGA: Semantic Guided Attention on Visual Prototype for Few-Shot Learning
https://github.com/masakhane-io/masakhane-news
공식 코드MasakhaNEWS: News Topic Classification for African languages
https://github.com/matt-su/dr-adapter
공식 코드Domain-Rectifying Adapter for Cross-Domain Few-Shot Segmentation
https://github.com/matthewcym/sflm
공식 코드Revisiting Self-Training for Few-Shot Learning of Language Model
https://github.com/mattochal/enhanced-outlier-logits
공식 코드EOL: Transductive Few-Shot Open-Set Recognition by Enhancing Outlier Logits
https://github.com/mattochal/imbalanced_fsl_public
공식 코드https://github.com/maudzung/SFA3D
공식 코드https://github.com/maxlchen/Speech-PreFinetuning
공식 코드https://github.com/maxwellyaoni/pace
공식 코드PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularization
https://github.com/maybenotime/css
공식 코드A New Dataset and Empirical Study for Sentence Simplification in Chinese
https://github.com/mayug/basetransformers
공식 코드BaseTransformers: Attention over base data-points for One Shot Learning
https://github.com/mbonto/fewshot_generalization
공식 코드https://github.com/mbonto/fewshot_neuroimaging_classification
공식 코드https://github.com/mboudiaf/RePRI-for-Few-Shot-Segmentation
공식 코드Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?
https://github.com/mboudiaf/TIM
공식 코드https://github.com/mboudiaf/pytorch-meta-dataset
공식 코드https://github.com/mdausort/Cytology-fine-tuning
공식 코드Exploring Foundation Models Fine-Tuning for Cytology Classification
https://github.com/megvii-research/DPGN
공식 코드DPGN: Distribution Propagation Graph Network for Few-shot Learning
https://github.com/mennafateen/modular-asas-f-rag
공식 코드Beyond Scores: A Modular RAG-Based System for Automatic Short Answer Scoring with Feedback
https://github.com/menyifang/dct-net
공식 코드DCT-Net: Domain-Calibrated Translation for Portrait Stylization
https://github.com/mesaysama/amharic-handwritten-character-dataset
공식 코드Offline Handwritten Amharic Character Recognition Using Few-shot Learning
https://github.com/mesophil/myriadal
공식 코드https://github.com/mgkumar138/Oneshot_Reservoir
공식 코드One-shot learning of paired association navigation with biologically plausible schemas
https://github.com/mgkumar138/schema4one
공식 코드One-shot learning of paired association navigation with biologically plausible schemas
https://github.com/mgradyn/AniWho
공식 코드AniWho : A Quick and Accurate Way to Classify Anime Character Faces in Images
https://github.com/mibastro/VPE
공식 코드Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images
https://github.com/miccaiif/top
공식 코드https://github.com/michalislazarou/a2lp
공식 코드Adaptive Anchor Label Propagation for Transductive Few-Shot Learning
https://github.com/michalislazarou/ilpc
공식 코드Iterative label cleaning for transductive and semi-supervised few-shot learning
https://github.com/michalislazarou/tfh_fewshot
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.