A Machine Learning-Driven Solution for Denoising Inertial Confinement Fusion Images

arXiv:2511.16717 · cs.CV, cs.AI · Submitted 2025-11-20 · Read on arXiv

cs.CV, cs.AI

Submitted: 2025-11-20

Updated: 2026-09-01

Terminology

Aperture — 20× in this paper · explained in 1 episode
Fidelity — 18× in this paper · explained in 2 episodes
Latent Space — 9× in this paper · explained in 7 episodes
Wavelet Transform — 9× in this paper · explained in 2 episodes
Reconstruction Error — 6× in this paper · explained in 2 episodes
Benchmarking — 5× in this paper · explained in 3 episodes
Regularization — 5× in this paper · explained in 3 episodes
Activation Function — 4× in this paper · explained in 1 episode
Mean Squared Error — 4× in this paper
National Ignition Facility — 4× in this paper
Accuracy — 2× in this paper · explained in 1 episode
Adaptability — 2× in this paper · explained in 1 episode
Alignment — 2× in this paper · explained in 1 episode
Loss Function — 2× in this paper · explained in 2 episodes
Robustness — 2× in this paper · explained in 5 episodes
Benchmark — 1× in this paper · explained in 2 episodes
Clustering — 1× in this paper · explained in 2 episodes
Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
Feature Extraction — 1× in this paper · explained in 2 episodes
Generalizability — 1× in this paper · explained in 6 episodes
Hybrid Models — 1× in this paper · explained in 2 episodes
Inverse Problem — 1× in this paper · explained in 2 episodes
Representation Learning — 1× in this paper · explained in 2 episodes
Transfer Learning — 1× in this paper · explained in 10 episodes
Transformer — 1× in this paper · explained in 2 episodes

Sources

Effects of the Nonlinearity in Activation Functions on the Performance of Deep Learning Models

Terminology

Sources

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