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Few-Shot Learning

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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/Wensi-Tang/DPSN

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Interpretable Time-series Classification on Few-shot Samples

https://github.com/WentaoChen0813/CDCS-FSL

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Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and Aligned Representations

https://github.com/WentaoChen0813/SemanticPrompt

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Semantic Prompt for Few-Shot Image Recognition

https://github.com/WonderSeven/ULDA

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Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation

https://github.com/Wuziyi616/EventCLIP

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EventCLIP: Adapting CLIP for Event-based Object Recognition

https://github.com/XHomL/COCA

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COCA: Classifier-Oriented Calibration via Textual Prototype for Source-Free Universal Domain Adaptation

https://github.com/Xiangyi1996/PPNet-PyTorch

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Part-aware Prototype Network for Few-shot Semantic Segmentation

https://github.com/XuMengyaAmy/CIDACaptioning

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Class-Incremental Domain Adaptation with Smoothing and Calibration for Surgical Report Generation

https://github.com/YDU-uva/VSM

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Learning to Learn Variational Semantic Memory

https://github.com/Yang-Bob/PMMs

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Prototype Mixture Models for Few-shot Semantic Segmentation

https://github.com/YangYongJin/APEX

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Leveraging Normalization Layer in Adapters With Progressive Learning and Adaptive Distillation for Cross-Domain Few-Shot Learning

https://github.com/YazhouZhu19/RobustEMD

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RobustEMD: Domain Robust Matching for Cross-domain Few-shot Medical Image Segmentation

https://github.com/Yikai-Wang/ICI-FSL

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Instance Credibility Inference for Few-Shot Learning

https://github.com/Yussef93/FewShotCellSegmentation

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Few-Shot Microscopy Image Cell Segmentation

https://github.com/Yuxin-CV/DTN

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Diversity Transfer Network for Few-Shot Learning

https://github.com/Ze-Yang/Context-Transformer

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Context-Transformer: Tackling Object Confusion for Few-Shot Detection

https://github.com/ZhangYuanhan-AI/NOAH

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Neural Prompt Search

https://github.com/ZhaoZhibin/DL-based-Intelligent-Diagnosis-Benchmark

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Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study

https://github.com/Zhen-ao/BSDB-CDFSL_Track

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Ensemble Model with Batch Spectral Regularization and Data Blending for Cross-Domain Few-Shot Learning with Unlabeled Data

https://github.com/ZhixiuYe/MLMAN

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Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification

https://github.com/ZihaoWang/Few-shot-KGC

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Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion

https://github.com/ZihaoZhao/SpikeGatingFlow

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The Spike Gating Flow: A Hierarchical Structure Based Spiking Neural Network for Online Gesture Recognition

https://github.com/a-moin/flexemg

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An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier

https://github.com/aHapBean/PCP-MAE

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PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders

https://github.com/abdumaa/hiqualprop

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HQP: A Human-Annotated Dataset for Detecting Online Propaganda

https://github.com/abhi4ssj/few-shot-segmentation

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'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images

https://github.com/abrazinskas/FewSum

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Few-Shot Learning for Opinion Summarization

https://github.com/abrazinskas/selsum

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Learning Opinion Summarizers by Selecting Informative Reviews

https://github.com/acctouhou/frankenstein_optimizer

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Frankenstein Optimizer: Harnessing the Potential by Revisiting Optimization Tricks

https://github.com/adamjelley/poem

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Contrastive Meta-Learning for Partially Observable Few-Shot Learning

https://github.com/adapter-hub/playground

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AdapterHub Playground: Simple and Flexible Few-Shot Learning with Adapters

https://github.com/adipandas/one-shot-steel-surfaces

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One-Shot Recognition of Manufacturing Defects in Steel Surfaces

https://github.com/adriangonz/statistical-nlp-17

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One-Shot Learning for Language Modelling

https://github.com/afshinrahimi/mmner

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Massively Multilingual Transfer for NER

https://github.com/ai-forever/mgpt

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mGPT: Few-Shot Learners Go Multilingual

https://github.com/aielawady/relic

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ReLIC: A Recipe for 64k Steps of In-Context Reinforcement Learning for Embodied AI

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