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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/usc-gvl/agent-driver

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A Language Agent for Autonomous Driving

https://github.com/ustc-starteam/chemeval

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ChemEval: A Comprehensive Multi-Level Chemical Evaluation for Large Language Models

https://github.com/utcsilab/fewmax

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Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation Learning

https://github.com/utkarshnath/Adjoint-Network

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Adjoined Networks: A Training Paradigm with Applications to Network Compression

https://github.com/valeoai/BF3S

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Boosting Few-Shot Visual Learning with Self-Supervision

https://github.com/vbeliveau/radiology-text-classification

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Classification of Radiological Text in Small and Imbalanced Datasets in a Non-English Language

https://github.com/vcl-iisc/robust-few-shot-learning

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Robust Few-shot Learning Without Using any Adversarial Samples

https://github.com/vgsatorras/few-shot-gnn

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Few-Shot Learning with Graph Neural Networks

https://github.com/vico-uoe/universalrepresentations

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Universal Representations: A Unified Look at Multiple Task and Domain Learning

https://github.com/vinuni-vishc/Few-Shot-Cosine-Transformer

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Enhancing Few-shot Image Classification with Cosine Transformer

https://github.com/vision-kek/abcdfss

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Adapt Before Comparison: A New Perspective on Cross-Domain Few-Shot Segmentation

https://github.com/visionml/pytracking

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Learning What to Learn for Video Object Segmentation

https://github.com/vita-group/layergraftedpretraining_iclr23

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Layer Grafted Pre-training: Bridging Contrastive Learning And Masked Image Modeling For Label-Efficient Representations

https://github.com/vitae-transformer/remote-sensing-rvsa

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Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model

https://github.com/vitae-transformer/vitae-transformer-remote-sensing

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Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model

https://github.com/vityavitalich/taxollama

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TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Semantic Tasks

https://github.com/vkverma01/Zero-Shot

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A Simple Exponential Family Framework for Zero-Shot Learning

https://github.com/vkverma01/Zero-Shot-Learning

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A Simple Exponential Family Framework for Zero-Shot Learning

https://github.com/vturrisi/disef

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Diversified in-domain synthesis with efficient fine-tuning for few-shot classification

https://github.com/vuoristo/MMAML

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Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation

https://github.com/vuoristo/MMAML-Regression

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Covariate Distribution Aware Meta-learning

https://github.com/vvorakit/Entity-Aspect-Sentiment-Triplet-Extraction

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Deep Content Understanding Toward Entity and Aspect Target Sentiment Analysis on Foundation Models

https://github.com/wangjingyao07/adaptive-sampler

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Towards Task Sampler Learning for Meta-Learning

https://github.com/wangjlgz/MetaTL

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Sequential Recommendation for Cold-start Users with Meta Transitional Learning

https://github.com/wangkiw/teen

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Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration

https://github.com/wangren09/MetaAdv

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On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning

https://github.com/wangtz19/NetMamba

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NetMamba: Efficient Network Traffic Classification via Pre-training Unidirectional Mamba

https://github.com/wangxinyilinda/concept-based-demonstration-selection

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Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning

https://github.com/wbw520/MTUNet

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Match Them Up: Visually Explainable Few-shot Image Classification

https://github.com/wenbinlee/defensivefsl

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Defensive Few-shot Learning

https://github.com/wenbinlee/trip-roma

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Trip-ROMA: Self-Supervised Learning with Triplets and Random Mappings

https://github.com/wenchen-chen/s4l-fsc

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Spectral-Spatial Self-Supervised Learning for Few-Shot Hyperspectral Image Classification

https://github.com/wendybaiyunwei/fsl-rectifier-pub

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FSL-Rectifier: Rectify Outliers in Few-Shot Learning via Test-Time Augmentation

https://github.com/wengbenjue/llms-peft-cook

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Navigating the Landscape of Large Language Models: A Comprehensive Review and Analysis of Paradigms and Fine-Tuning Strategies

https://github.com/whaleloops/clinicalmamba

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ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical Notes

https://github.com/whileherham/imr-hsnet

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Iterative Few-shot Semantic Segmentation from Image Label Text

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