Popular GitHub Repositories for Rare

Text recognition (optical character recognition) with deep learning methods, ICCV 2019

Using an LLM to discover the genetic causes of rare disease

Machine Learning for Computer Security

SHEPHERD: Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases

Rare disease identification from free-text clinical notes with ontologies and weak supervision

[AAAI 2022] RareGAN: Generating Samples for Rare Classes

This is a repo to implement Anomaly Detection which is the technique of identifying rare events or observations which can raise suspicions by being statistically different from the rest of the observations.

Statistics, signal processing, finance, econometrics, manufacturing, networking[disambiguation needed] and data mining, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.

A comprehensive comparison of multimodal models - llama3.2-vision, minicpm-v, llava-llama3, llava, llava13:b and closed source models for animal classification tasks. This project evaluates various models' performance in classifying 10 different animal species, ranging from common to rare animals.

AI Diagnosis Assistant for Diagnosing Rare Diseases

Rare Refusal-Reply LLM generator. Aimed at generating a new model based on an existing one, with almost the same performance but a lower rate of refusal replies.

AL for rare class strategies compared in the paper "Transfer and Active Learning for Dissonance Detection: Addressing the Rare Class Challenge" adapted, to be used out-of-the-box.

Welcome to the Federated Learning for Rare Genetic Disorder Classification project! This repository demonstrates the application of Federated Learning (FL) techniques to classify rare genetic disorders using Electronic Health Record (EHR) data while addressing two critical challenges: privacy and limited data availability.

Hybrid system for automatic log classification: uses Regex for known patterns, BERT for logs with training data, and Deepseek R1 LLM for rare cases. Boosts accuracy and speeds up error, threat, and event detection for enterprise IT operations.

Detection of rare child diseases by applying graph machine learning to a remote dataset with federated machine learning

It has never been rare among AI researchers that combine and evaluate different models to discover their methods. When I read about Retinanet and Efficientnet, I had a mind to combine them.

Developing explainable multimodal AI models with hands-on lab on the life-cycle of rare event prediction in manufacturing @ AAAI-25

FastText is a word embedding model by Facebook that captures word meanings, even for rare words, by using subword information, improving over traditional methods like word2vec.

This project processes text files to identify hapax legomena (words that appear only once) and saves the results in an Excel file. It uses tokenization, optional lemmatization, and frequency analysis to extract and list these rare words.