Model Training
Adversarial Training
Teaching an AI to defend itself by constantly attacking it with tricky or malicious inputs during training.
Definition
A training technique where models are exposed to adversarial examples, inputs deliberately crafted to fool the model, to improve robustness. Widely used to harden AI systems against malicious attacks.
Why it matters
Critical for security-sensitive AI (self-driving cars, facial recognition) to prevent hacks via manipulated inputs.
Related terms in Model Training
Autoencoders
A neural network that learns to compress data into a small code and then unzip it back to the original.
Distillation (Model Distillation)
Teaching a small, fast AI model to mimic a large, expensive one, so you get similar results at a fraction of the cost.
Dropout
Randomly turning off some neurons during training so the AI doesn't over-memorize and can generalize better.
Epoch
One complete pass through the entire training dataset, the AI sees every example once per epoch.
From vocabulary to outcomes
Ready to put Adversarial Training to work?
Knowing the term is step one. Deploying it inside a revenue architecture that compounds is what Sophizo builds.
Book a Discovery Call