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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/liulu112601/PPN

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Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph

https://github.com/liulu112601/Prototype-Propagation-Networks

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Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph

https://github.com/liutongkun/gfds

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Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models

https://github.com/liuweide01/HBNC

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Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation

https://github.com/liuyuanwei98/ipmt

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Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation

https://github.com/livingingroups/animal2vec

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animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacoustics

https://github.com/livreq/meta-sgld

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Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis

https://github.com/lizhecheng02/vlm4font

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Texture or Semantics? Vision-Language Models Get Lost in Font Recognition

https://github.com/lizhh268/FSSUWNet

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FSSUWNet: Mitigating the Fragility of Pre-trained Models with Feature Enhancement for Few-Shot Semantic Segmentation in Underwater Images

https://github.com/lmsdss/metamodulation

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MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer Tasks

https://github.com/loadder/ett_tmlr2022

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Exploring Efficient Few-shot Adaptation for Vision Transformers

https://github.com/locuslab/flyp

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Finetune like you pretrain: Improved finetuning of zero-shot vision models

https://github.com/louisegan514/hidden-entity-detection-from-github-leveraging-llms

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Hidden Entity Detection from GitHub Leveraging Large Language Models

https://github.com/lovelyqian/CDFSOD-benchmark

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Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector

https://github.com/lovelyqian/ME-D2N_for_CDFSL

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ME-D2N: Multi-Expert Domain Decompositional Network for Cross-Domain Few-Shot Learning

https://github.com/lovelyqian/Meta-FDMixup

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Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data

https://github.com/lovelyqian/ntire2025_cdfsod

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NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results

https://github.com/lovelyqian/styleadv-cdfsl

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StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning

https://github.com/lovelyqian/wave-SAN-CDFSL

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Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning

https://github.com/lucashancef/e-sort

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E-Sort: Empowering End-to-end Neural Network for Multi-channel Spike Sorting with Transfer Learning and Fast Post-processing

https://github.com/lucashancef/fafesort

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E-Sort: Empowering End-to-end Neural Network for Multi-channel Spike Sorting with Transfer Learning and Fast Post-processing

https://github.com/lucfra/FAR-HO

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A Bridge Between Hyperparameter Optimization and Learning-to-learn

https://github.com/luck1226/multisource_deepGaussianProcess

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Conditional Deep Gaussian Processes: multi-fidelity kernel learning

https://github.com/luckybian/isy5001

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IntellectSeeker: A Personalized Literature Management System with the Probabilistic Model and Large Language Model

https://github.com/luhc666/asc-cdfsl

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Adaptive Semantic Consistency for Cross-domain Few-shot Classification

https://github.com/lukashedegaard/dage

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Supervised Domain Adaptation: A Graph Embedding Perspective and a Rectified Experimental Protocol

https://github.com/luoxiaoliu/pfenet2plus

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PFENet++: Boosting Few-shot Semantic Segmentation with the Noise-filtered Context-aware Prior Mask

https://github.com/luoyuanlab/clinical-longformer

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Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences

https://github.com/lyan62/data-curation

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The Role of Data Curation in Image Captioning

https://github.com/lyymuwu/timo

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Text and Image Are Mutually Beneficial: Enhancing Training-Free Few-Shot Classification with CLIP

https://github.com/lzw108/raemollm

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RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning based on Emotional Information

https://github.com/lzzcd001/OSLSM

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One-Shot Learning for Semantic Segmentation

https://github.com/m-3lab/im-iad

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IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing

https://github.com/m-3lab/open-iad

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IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing

https://github.com/machine-listeners-valencia/fsl_osr_dataset_baseline

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An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments

https://github.com/manasgg44/improved_proto

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Adaptive Prototypical Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.