Adversarial-Training
Adversarial training is a technique used in machine learning to enhance the robustness of models against adversarial examples—inputs intentionally designed to deceive the model into making incorrect predictions. This method involves incorporating adversarial examples into the training process, allowing the model to learn from these deceptive inputs. There are two primary approaches: one involves retraining the model with previously identified adversarial examples, while the other integrates perturbations directly into the training data. By doing so, adversarial training aims to improve the model’s generalization and resilience, making it less susceptible to various types of adversarial attacks.
Everything you need to know about Adversarial Training in NLP
Adversarial training is a fairly recent but very exciting field in Machine Learning. Since Adversarial Examples were first introduced by Christian Szegedy[1] back in 2013, they have brought to light…
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Adversarial Examples
An adversarial example is an instance with small, intentional feature perturbations that cause a machine learning model to make a false prediction. I recommend reading the chapter about Counterfactual...
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Adversarial Example Generation
Threat Model For context, there are many categories of adversarial attacks, each with a different goal and assumption of the attacker’s knowledge. However, in general the overarching goal is to add th...
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About Adversarial Examples
Adversarial examples are an interesting topic in the world of deep neural networks. This post will try to address some basic questions on the topic including how to generate such examples and defend…
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Adversarial Examples — Rethinking the Definition
Adversarial examples are a large obstacle for a variety of machine learning systems to overcome. Their existence shows the tendency of models to rely on unreliable features to maximize performance…
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Adversarial Validation
If you were to study some of the competition-winning solutions on Kaggle, you might notice references to “adversarial validation” (like this one). What is it? In short, we build a classifier to try…
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