Papers with Code 아카이브
Few-Shot Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 12
- 평가표
- 27
- 보존된 평가 기록
- 52
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/baiksung/L2F
공식 코드https://github.com/baiksung/MeTAL
공식 코드Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning
https://github.com/basiralab/GmTE-Net
공식 코드https://github.com/basiralab/multigraphgnet
공식 코드https://github.com/basiralab/one-representative-shot-learning
공식 코드https://github.com/bbbdylan/unisiam
공식 코드https://github.com/bcmi/Awesome-Weak-Shot-Learning
공식 코드Weak Novel Categories without Tears: A Survey on Weak-Shot Learning
https://github.com/bdvllrs/generalization-vision
공식 코드https://github.com/beepkh/supmer
공식 코드Self-supervised Meta-Prompt Learning with Meta-Gradient Regularization for Few-shot Generalization
https://github.com/beierzhu/prompt-align
공식 코드https://github.com/bethgelab/siamese-mask-rcnn
공식 코드https://github.com/bghojogh/Siamese-Network-Histopathology
공식 코드Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study
https://github.com/bhrqw/sada
공식 코드Few-Shot Learning with Visual Distribution Calibration and Cross-Modal Distribution Alignment
https://github.com/bids-xu-lab/clinical_entity_recognition_using_gpt_models
공식 코드Improving Large Language Models for Clinical Named Entity Recognition via Prompt Engineering
https://github.com/bids-xu-lab/me-llama
공식 코드Me LLaMA: Foundation Large Language Models for Medical Applications
https://github.com/bigdata-inha/zero-base-gfsl
공식 코드https://github.com/bilaliman/rv_explainability
공식 코드Generating Zero-shot Abstractive Explanations for Rumour Verification
https://github.com/bingykang/Fewshot_Detection
공식 코드https://github.com/biomedical-data-analysis-laboratory/adnet-for-ais-segmentation
공식 코드Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation
https://github.com/biomistral/biomistral
공식 코드BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains
https://github.com/bl0/negative-margin.few-shot
공식 코드Negative Margin Matters: Understanding Margin in Few-shot Classification
https://github.com/boschresearch/metanas
공식 코드https://github.com/bosonphoton/abstractedgaussianprototypes
공식 코드Abstracted Gaussian Prototypes for One-Shot Concept Learning
https://github.com/brain-cog-lab/n-omniglot
공식 코드N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning
https://github.com/brando90/Does-MAML-Only-Work-via-Feature-Re-use-A-Data-Set-Centric-Perspective
공식 코드Does MAML Only Work via Feature Re-use? A Data Centric Perspective
https://github.com/brandon3964/multimodal-task-vector
공식 코드Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning
https://github.com/breakaway7/p3dc-shot
공식 코드P3DC-Shot: Prior-Driven Discrete Data Calibration for Nearest-Neighbor Few-Shot Classification
https://github.com/brendenlake/omniglot
공식 코드https://github.com/brettfyt/ss_via_llms
공식 코드https://github.com/brinkl/graphhetnet
공식 코드https://github.com/brjathu/SKD
공식 코드Self-supervised Knowledge Distillation for Few-shot Learning
https://github.com/bugrabaran/semantics-driven-fsl
공식 코드Semantics-driven Attentive Few-shot Learning over Clean and Noisy Samples
https://github.com/buoncubi/fuzzy_sit
공식 코드https://github.com/bupt-ai-cz/restnet
공식 코드RestNet: Boosting Cross-Domain Few-Shot Segmentation with Residual Transformation Network
https://github.com/buptxiaofeng/proxynet_fsl
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.