Real-World Super-Resolution via Kernel Estimation and Noise Injection
-
Updated
Sep 2, 2020 - Python
Real-World Super-Resolution via Kernel Estimation and Noise Injection
Code for "DeepDRR: A Catalyst for Machine Learning in Fluoroscopy-guided Procedures". https://arxiv.org/abs/1803.08606
Noise Injection Techniques provides a comprehensive exploration of methods to make machine learning models more robust to real-world bad data. This repository explains and demonstrates Gaussian noise, dropout, mixup, masking, adversarial noise, and label smoothing, with intuitive explanations, theory, and practical code examples.
NINJA: Noise Inject agent tool to expose subtle and unintended message races
Verification harness for quantum ML. A reproducible lab for stress-testing quantum models where predictive accuracy, identifiability, curvature, and robustness under noise can diverge.
EVT-based noise injection toolkit for evaluating time series forecasting robustness
Noise-aware ML pipeline for large-scale agricultural yield prediction using PySpark and LightGBM, with feature and label noise simulation, mitigation, and distributed training.
Time series forecasting and classification using shared linear backbone with task-specific heads and forgetting mechanisms
Can language models recognize perturbations applied to their activations? Study this question via localization, classification, and in-context learning experiments.
Scripts to inject noise into Google's GREAT code dataset to study memorization in neural code models.
A modular IDS leveraging multi-sensor correlation, sliding-window analysis, statistical Z-score anomaly detection, EMA-based SYN flood detection, and rule-based heuristics. Features deduplication, noise resilience, and severity control, enabling accurate real-time detection of scans, brute-force attacks, and multi-stage intrusions.
🔍 Enhance model robustness with noise injection techniques to tackle messy, real-world data and improve machine learning performance.
📧 Detect spam emails easily using machine learning with TF-IDF Vectorizer and Naive Bayes for accurate and efficient filtering.
📧 Detect spam emails with ease using machine learning and the Naive Bayes algorithm for fast, accurate results.
Add a description, image, and links to the noise-injection topic page so that developers can more easily learn about it.
To associate your repository with the noise-injection topic, visit your repo's landing page and select "manage topics."