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Generates stochastic adversarial attacks against machine learning models by leveraging the latent space of Vector-Quantized Variational Autoencoders (VQ-VAEs) to produce diverse perturbations.
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With 0 stars and no forks, this is a personal experiment or academic project. While the specific approach of using VQ-VAEs for adversarial attacks is a valid niche in research, the lack of traction, documentation, or community makes it easily reproducible and provides no moat.
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