Recentstudieshaveshownthat the transferability of adversarial examples exists for CNNs, and the same holds true for ViTs.

We use the discrete variablezd,t to represent the topic that thetth word is generated from.

Bayesian model evidence gives a clear criteria for such model selection. However, computing model evidence requires integration over the likelihood, which is challenging, particularly when the likelihood is non-closed-form and/or expensive.

However, statements different from the original statement could also be logically consistent with respecttoagivencontext.

Theconventional closed-set DAmethods generally assume that the source and target domains share the same label space. However, this assumption is often not realistic in practice.

Despite its potential benefits, applying DST to GANs presents challenges due to the adversarial nature of the training process.