Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow

Implementation of Prototypical Networks for Few Shot Learning (https://arxiv.org/abs/1703.05175) in Pytorch

A few shot learning repository for bearing fault diagnosis.

Few-shot classification in Named Entity Recognition Task

Few-Shot Keyword Spotting

This repo contains the implementation of some new papers on some advanced topics of machine learning e.g. meta-learning, reinforcement-learning, meta-reinforcement-learning, continual-learning and etc.

A novel method for few shot learning

Implementation of Prototypical Networks for Few-shot Learning in TensorFlow 2.0

Deepest Season 6 Meta-Learning study papers plus alpha

Official code of the CVPR 2022 paper "Proto2Proto: Can you recognize the car, the way I do?"

[TPAMI 2025] Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable and Trustworthy Image Recognition

PyTorch implementation for "ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback" (https://arxiv.org/abs/2107.14035).

Official repository for the paper "ProtoASNet: Dynamic Prototypes for Inherently Interpretable and Uncertainty-Aware Aortic Stenosis Classification in Echocardiography" in MICCAI 2023 Conference

Prototypical Networks for the task of few-shot image classification on Omniglot and mini-ImageNet.

Code containing implementation of prototypical networks paper with a few tweaks

Official Implementation of "SPN: Stable Prototypical Network for Few-Shot Learning-Based Hyperspectral Image Classification" (GRSL22)

Official implementation of "Extreme Value Meta-Learning for Few-Shot Open-Set Recognition of Hyperspectral Images" (TGRS'23)

Few-shot bearing fault diagnosis using multimodal LLMs and prototypical networks

GUI based tool to train and develop Few Shot Classification ML model.

We explore different techniques to perform few-shot-classification of fashion images.

The code for "Efficient-PrototypicalNet with self knowledge distillation for few-shot learning"

A project to test MCP voulnerabilities and defense strategies in a cloud-native Dockerized environment. This repository is part of my Bsc thesis in computer engineering at Óbuda University.

A lightweight PyTorch implementation of Prototypical Networks using a ViT-Small/16 backbone for few-shot classification on standard benchmarks (Mini-ImageNet, CUB-200, CIFAR-FS, FC100). Includes both production scripts for training & evaluation and Jupyter notebooks for interactive analysis.

Few-shot Learning for Fine-grained Flower Classification with Prototypical Networks

Implementation of Meta Learning Methods via Torchmeta framework

Prototypical Networks on Omniglot Dataset for Few-Shot Classification

A study on the interpretability of the concepts learned by Prototypical Part Networks (ProtoPNets) on the CUB200-2011 and CelebAMask datasets.

Prototypical Networks for Information Extraction in Visual Documents. Weights can be found at https://drive.google.com/file/d/1Zrp7QaZIf0H_FFRx_LhB0uZTqDUSis2H/view?usp=sharing.

Image classification with very few data sample (n=25 per class)

A streamlit web app that allows you to train Few Shot image classification models