I'm currently getting more into the topic of GANs and Generating Models. I've understood how the Generator and Discriminator work together in optimization to generate synthetic samples. Now I'm wondering how the model learns to reflect the occurance frequencies of the true entries in case of categorical data. As an example, lets say we have two columns of entries (1,A), (1, A), (2, A), (2, B) The model, when trained would not only try to output real combinations, so e.g. …
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 I find such papers?
For untargeted attacks, how does one know if the decrease in accuracy is due to an adversarial perturbation or its just being attributed to adding noise to input.
Is there a connection between: "Adversarial Learning" (AL) and "Generative Adversarial Networks" (GANs)? Is it valid to say that GANs employ AL?
This question is based on the following intuition: To my understanding adversarial attacks work, because the model is stuck in a local minima and the adversarial attack finds this with gradient descent. Could this be used to train a Neural Network that is able to generalize better? This way the model would be trained on exactely the examples it completely misunderstands. Intuitively it feels like the teacher trying to find where the student misunderstood the topic and than correcting it …
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 instances by disrupting my points so that they come out just behind my hyperplane. That is to say ensure that the points classified as 0 go to 1 and those classified as 1 go to 0. I would like to find the minimal perturbation …
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: referring the original paper. My understanding more correlates with the second paper scheme, as: Where there is two dot-product operations and three hidden parametric matrices: $$W_k, W_v, W_q$$ which corresponds to $W_f, W_g, W_h$ without $W_v$ as it in the original paper explanation, which is as following: Is this a mistake in the original paper ?
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 together a search engine server with a use policy that permits constant queries being sent by paid customers. The search engine's client transmits, at some frequency, generated search queries (e.g. markov, ML-generated, random dictionary words, sourced from news, whatever; up to you) in order to …
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?