Real-World Super-Resolution via Kernel Estimation and Noise Injection
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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.
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.
Can language models recognize perturbations applied to their activations? Study this question via localization, classification, and in-context learning experiments.
🔍 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.
Time series forecasting and classification using shared linear backbone with task-specific heads and forgetting mechanisms
📧 Detect spam emails with ease using machine learning and the Naive Bayes algorithm for fast, accurate results.
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