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pro vyhledávání: '"Müller, Nicolas Michael"'
Autor:
Casanova, Edresson, Shulby, Christopher, Gölge, Eren, Müller, Nicolas Michael, de Oliveira, Frederico Santos, Junior, Arnaldo Candido, Soares, Anderson da Silva, Aluisio, Sandra Maria, Ponti, Moacir Antonelli
In this paper, we propose SC-GlowTTS: an efficient zero-shot multi-speaker text-to-speech model that improves similarity for speakers unseen during training. We propose a speaker-conditional architecture that explores a flow-based decoder that works
Externí odkaz:
http://arxiv.org/abs/2104.05557
Adversarial data poisoning is an effective attack against machine learning and threatens model integrity by introducing poisoned data into the training dataset. So far, it has been studied mostly for classification, even though regression learning is
Externí odkaz:
http://arxiv.org/abs/2009.07008
Publikováno v:
2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, 2019
A key requirement for supervised machine learning is labeled training data, which is created by annotating unlabeled data with the appropriate class. Because this process can in many cases not be done by machines, labeling needs to be performed by hu
Externí odkaz:
http://arxiv.org/abs/1912.05283