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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/SLAMPAI/large-scale-pretraining-transfer
공식 코드https://github.com/SamyAteia/BioRAGent
공식 코드https://github.com/Sara-Ahmed/SiT
공식 코드https://github.com/SawanKumar28/pero
공식 코드Reordering Examples Helps during Priming-based Few-Shot Learning
https://github.com/Seokju-Cho/Volumetric-Aggregation-Transformer
공식 코드https://github.com/Sha-Lab/CASTLE
공식 코드Learning Adaptive Classifiers Synthesis for Generalized Few-Shot Learning
https://github.com/Sha-Lab/FEAT
공식 코드Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions
https://github.com/Shiyan-cps/PointKAN-pytorch
공식 코드https://github.com/ShuaiLYU/MVREC
공식 코드MVREC: A General Few-shot Defect Classification Model Using Multi-View Region-Context
https://github.com/ShuoYang-1998/Few_Shot_Distribution_Calibration
공식 코드https://github.com/ShuoYang-1998/ICLR2021-Oral_Distribution_Calibration
공식 코드https://github.com/SkBlaz/tax2vec
공식 코드tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification
https://github.com/SteveImmanuel/OEM-Few-Shot-Learnable-Prompt
공식 코드Learnable Prompt for Few-Shot Semantic Segmentation in Remote Sensing Domain
https://github.com/StoneT2000/trajectorytranslation
공식 코드Abstract-to-Executable Trajectory Translation for One-Shot Task Generalization
https://github.com/SunJiamei/few-shot-lrp-guided
공식 코드Explanation-Guided Training for Cross-Domain Few-Shot Classification
https://github.com/SunjHan/Hybrid-Representation-Learning-Approach-for-Rare-Disease-Classification
공식 코드https://github.com/SuperSecretBioTech/evolutionary_monte_carlo_search
공식 코드Improving few-shot learning-based protein engineering with evolutionary sampling
https://github.com/THUDM/ScenarioMeta
공식 코드Sequential Scenario-Specific Meta Learner for Online Recommendation
https://github.com/TJU-YDragonW/TAMT
공식 코드TAMT: Temporal-Aware Model Tuning for Cross-Domain Few-Shot Action Recognition
https://github.com/Tagore-7/Small-Language-Models-for-the-Democratization-of-Financial-Literacy
공식 코드https://github.com/TalSchuster/CrossLingualELMo
공식 코드https://github.com/TencentAILabHealthcare/Few-shot-WSI
공식 코드https://github.com/ThomasGoerttler/similarity-analysis-of-maml
공식 코드Exploring the Similarity of Representations in Model-Agnostic Meta-Learning
https://github.com/ToFec/OneShotImageRegistration
공식 코드One Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking
https://github.com/TrinitialChan/DifFSS
공식 코드https://github.com/TristanThrush/few-shot-lm-learning
공식 코드https://github.com/Tsingularity/FRN
공식 코드Few-Shot Classification with Feature Map Reconstruction Networks
https://github.com/Tung-I/Dual-awareness-Attention-for-Few-shot-Object-Detection
공식 코드https://github.com/TungChintao/APM
공식 코드Lightweight Frequency Masker for Cross-Domain Few-Shot Semantic Segmentation
https://github.com/UKPLab/arxiv-2024-llm-trends
공식 코드https://github.com/UMass-Foundation-Model/VisualCoT
공식 코드https://github.com/VICO-UoE/URL
공식 코드https://github.com/WENGSYX/Self-Verification
공식 코드Large Language Models are Better Reasoners with Self-Verification
https://github.com/WOW5678/CoGraph
공식 코드Few-Shot Electronic Health Record Coding through Graph Contrastive Learning
https://github.com/WangYueFt/rfs
공식 코드Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?
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