OneShot Learning-based hotword detection.

FSL-Mate: A collection of resources for few-shot learning (FSL).

Code for RA-L paper "One-shot Learning for Task-oriented Grasping"

Implementation of Facial Recognition System Using Facenet based on One Shot Learning Using Siamese Networks

Demonstration of LLM techniques such as prompt engineering, full finetuning, PEFT (LoRA) etc.

Papers and code related to 'Less Than One'-Shot (LO-Shot) Learning

Find logos in images and videos in just one-shot. Never be embarrassed again to say that you have a small data situation!

A machine-learnable version of the python object system, with support for one-shot learning.

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

Some State-of-the-Art few shot learning algorithms in tensorflow 2

Implementation of One-Shot Object Detection with Co-Attention and Co-Excitation in Pytorch

Data-Free Ensemble Selection For One-Shot Federated Learning

Face Recognition using Siamese(Twin) Network along with Triplet Loss.

LittleAdversary is an adversarial machine learning library made to aid research into adversarial attacks and defences, with a primary focus on one-shot defences. It contains an end-to-end implementation of the proposed defence in 'Siamese Neural Networks for Adversarial Robustness ', complete with statistical analysis of the results.

This project aims to develop an innovative anomaly detection system using advanced data mining and deep learning techniques to accurately identify and localize defects in manufacturing components, thereby enhancing quality control processes and reducing production losses.

Official implementation of the paper: Learn to aggregate global and local representations for few-shot learning

The source code for Multi-Scale Kronecker-Product Relation Networks for Few-Shot Learning

한국어 기반 One-shot video tuning with Stable Diffusion

One-shot Siamese Neural Network, using TensorFlow 2.0, based on the work presented by Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov. we used the “Labeled Faces in the Wild” dataset with over 5,700 different people. Some people have a single image, while others have dozens.

SmartFew is your swiss knife for semi-supervised structuring of unlabeled data using Few Shot Learning.