Questions tagged [adversarial-ml]
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9 questions
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About GANs specialization
I'm working in a binary classification problem. In general terms, I'm focused on using neural networks to classify datasets that are not huge and in order to address that problem I'm comparing ...
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Why do object detection model adversarial masks look different from those of image classifiers?
I was messing around to observe the behavior for adversarial attacks on image classifiers, and decided to try it with an object detector as well. I realize that inference time attacks are more complex ...
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How to work with multiple feature types on autoencoder?
This is my first post here.
I am working on an adversarial autoencoder that receives different features, encodes them, and decodes them.
For instance, suppose you have a dataset from a large survey ...
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Basic and fundamental papers in Adversarial Attacks with code
I'm looking for basic and fundamental academic papers in Adversarial Attacks or defense. The attack or defense algorithm should be be easy to understand and the code can be found in Python. Where can ...
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Connection between GANs and adversarial learning
Is there a connection between: "Adversarial Learning" (AL) and "Generative Adversarial Networks" (GANs)? Is it valid to say that GANs employ AL?
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Creating adversarial sample against logistic regreession
I performed a binary classification using logistic regression.
My goal is the following:
I know the coefficient w of the hyperplane equation $y = wTx + b$. What I would like to do is create opposing ...
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SAGAN - what is the correct architecture?
Hi, in the original paper the following scheme of the self-attention appears:
https://arxiv.org/pdf/1805.08318.pdf
In a later overview:
https://arxiv.org/pdf/1906.01529.pdf
this scheme appears:
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Could you generate search queries to poison data analysis by a search engine?
A simple problem with search engines is that you have to trust that they will not build a profile of search queries you submit. (Without Tor or e.g. homomorphic encryption, that is.)
Suppose we put ...
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Intuitive explanation of Adversarial machine learning
How would you explain Adversarial machine learning in simple layman terms for a non-STEM person? What are the main ideas behind Adversarial machine learning?
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