Archives for adversarial training
Many machine learning models, including neural networks, consistently misclassify the adversarial examples. Adversarial examples are nothing but specialised inputs created to confuse neural networks, ultimately resulting in misclassification of the result. These notorious inputs are almost the same as the original image to human eyes but cause a neural network to fail to identify the image’s content.
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Adversarial inputs, also known as machine learning’s optical illusions, are inputs to the model an attacker has intentionally designed to confuse the algorithm into making a mistake. Such inputs can be typically dangerous for machines with a very low margin for risk. For instance, in self-driving cars, an attacker could target an autonomous vehicle with…
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The age of algorithmic innovations has now entered a new realm where the researchers are finding flaws in the techniques through adversarial attacks. In the case of computer vision problems, the role of adversarial attacks has been well established, and there have been several startups that are concentrating only on adversarial attacks. Any talk of…
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A self-driving car should be accurate — there is no room for second-guessing. A self-driving car’s accuracy improves drastically if it has been trained on data that has been annotated with parameters like colours, shapes, sizes, signs and angles. The question here is where can one get that kind of data? Today, data labelling has…
The post How Important Is Labelled Data For Improving Machine Learning Robustness? appeared first on Analytics India Magazine.
It is believed that Leonardo Da Vinci took more than a decade to paint a realistic version of Lisa del GIocondo which also happens to be the world’s most famous portrait ‘Monalisa’. Da Vinci worked through his adult life making sculptures and studying human anatomy. His eye for detail led to the most realistic reconstruction…
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