3 accepted papers
We propose Factorized-VITS, an advanced end-to-end text-to-speech model that incorporates explicit text-side prosody modeling control into VITS while achieving a clean factorization of the audio prior hidden space into text and prosody subspaces. Unlike previous works that rely on external or second…
Deep Neural Networks (DNNs) are known to be susceptible to adversarial attacks. Previous researches mainly focus on improving adversarial robustness in the fully supervised setting leaving the challenging domain of zero-shot adversarial robustness an open question. In this work we investigate this d…
"Deep learning-based object recognition systems can be easily fooled by various adversarial perturbations. One reason for the weak robustness may be that they do not have part-based inductive bias like the human recognition process. Motivated by this, several part-based recognition models have been…