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4 papers
Improving Diffusion Models's Data-Corruption Resistance using Scheduled Pseudo-Huber Loss
Artem Khrapov, Vadim Popov, Tasnima Sadekova +2
Diffusion models are known to be vulnerable to outliers in training data. In this paper we study an alternative diffusion loss function, which can preserve the high quality of gene…
A Semi-Supervised Deep Learning Approach to Dataset Collection for Query-By-Humming Task
Amantur Amatov, Dmitry Lamanov, Maksim Titov +3
Query-by-Humming (QbH) is a task that involves finding the most relevant song based on a hummed or sung fragment. Despite recent successful commercial solutions, implementing QbH s…
Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech
Vadim Popov, Ivan Vovk, Vladimir Gogoryan +2
Recently, denoising diffusion probabilistic models and generative score matching have shown high potential in modelling complex data distributions while stochastic calculus has pro…
Fine-tuning of Language Models with Discriminator
Vadim Popov, Mikhail Kudinov
Cross-entropy loss is a common choice when it comes to multiclass classification tasks and language modeling in particular. Minimizing this loss results in language models of very…