Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Fuentes Pineda, Gibran"'
Although the capacity of deep generative models for image generation, such as Diffusion Models (DMs) and Generative Adversarial Networks (GANs), has dramatically improved in recent years, much of their success can be attributed to computationally exp
Externí odkaz:
http://arxiv.org/abs/2401.09596
In this paper, we study the transferability of ImageNet spatial and Kinetics spatio-temporal representations to multi-label Movie Trailer Genre Classification (MTGC). In particular, we present an extensive evaluation of the transferability of ConvNet
Externí odkaz:
http://arxiv.org/abs/2210.07983
Background: Several studies have highlighted the importance of considering sex differences in the diagnosis and treatment of Acute Coronary Syndrome (ACS). However, the identification of sex-specific risk markers in ACS sub-populations has been scarc
Externí odkaz:
http://arxiv.org/abs/2101.01835
Verifying if two audio segments belong to the same speaker has been recently put forward as a flexible way to carry out speaker identification, since it does not require to be re-trained when new speakers appear on the auditory scene. Although many o
Externí odkaz:
http://arxiv.org/abs/2003.03432
Publikováno v:
In Biomedical Signal Processing and Control September 2023 86 Part A
Over the past decade, convolutional neural networks (CNNs) have become the driving force of an ever-increasing set of applications, achieving state-of-the-art performance. Most of the modern CNN architectures are composed of many convolutional and fu
Externí odkaz:
http://arxiv.org/abs/1909.06970
Publikováno v:
In Information Processing and Management May 2023 60(3)
Akademický článek
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In service robotics, there is an interest to identify the user by voice alone. However, in application scenarios where a service robot acts as a waiter or a store clerk, new users are expected to enter the environment frequently. Typically, speaker i
Externí odkaz:
http://arxiv.org/abs/1809.04115
The task of discovering topics in text corpora has been dominated by Latent Dirichlet Allocation and other Topic Models for over a decade. In order to apply these approaches to massive text corpora, the vocabulary needs to be reduced considerably and
Externí odkaz:
http://arxiv.org/abs/1807.00938