Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Carlucci, Fabio Maria"'
State-of-the-art results in deep learning have been improving steadily, in good part due to the use of larger models. However, widespread use is constrained by device hardware limitations, resulting in a substantial performance gap between state-of-t
Externí odkaz:
http://arxiv.org/abs/2111.03555
Explicit deep generative models (DGMs), e.g., VAEs and Normalizing Flows, have shown to offer an effective data modelling alternative for lossless compression. However, DGMs themselves normally require large storage space and thus contaminate the adv
Externí odkaz:
http://arxiv.org/abs/2111.01662
Autor:
Robbiano, Luca, Rahman, Muhammad Rameez Ur, Galasso, Fabio, Caputo, Barbara, Carlucci, Fabio Maria
Unsupervised Domain Adaptation (UDA) is a key issue in visual recognition, as it allows to bridge different visual domains enabling robust performances in the real world. To date, all proposed approaches rely on human expertise to manually adapt a gi
Externí odkaz:
http://arxiv.org/abs/2102.06679
Autor:
Bucci, Silvia, D'Innocente, Antonio, Liao, Yujun, Carlucci, Fabio Maria, Caputo, Barbara, Tommasi, Tatiana
Human adaptability relies crucially on learning and merging knowledge from both supervised and unsupervised tasks: the parents point out few important concepts, but then the children fill in the gaps on their own. This is particularly effective, beca
Externí odkaz:
http://arxiv.org/abs/2007.12368
Autor:
Lopes, Vasco, Carlucci, Fabio Maria, Esperança, Pedro M, Singh, Marco, Gabillon, Victor, Yang, Antoine, Xu, Hang, Chen, Zewei, Wang, Jun
The Neural Architecture Search (NAS) problem is typically formulated as a graph search problem where the goal is to learn the optimal operations over edges in order to maximise a graph-level global objective. Due to the large architecture parameter s
Externí odkaz:
http://arxiv.org/abs/1909.01051
Autor:
Carlucci, Fabio Maria, D'Innocente, Antonio, Bucci, Silvia, Caputo, Barbara, Tommasi, Tatiana
Human adaptability relies crucially on the ability to learn and merge knowledge both from supervised and unsupervised learning: the parents point out few important concepts, but then the children fill in the gaps on their own. This is particularly ef
Externí odkaz:
http://arxiv.org/abs/1903.06864
Autor:
Carlucci, Fabio Maria
Deep learning has raised hopes and expectations as a general solution for many applications; indeed it has proven effective, but it also showed a strong dependence on large quantities of data. Luckily, it has been shown that, even when data is scarce
Externí odkaz:
http://arxiv.org/abs/1902.04992
The effectiveness of generative adversarial approaches in producing images according to a specific style or visual domain has recently opened new directions to solve the unsupervised domain adaptation problem. It has been shown that source labeled im
Externí odkaz:
http://arxiv.org/abs/1705.08824
Despite the impressive progress brought by deep network in visual object recognition, robot vision is still far from being a solved problem. The most successful convolutional architectures are developed starting from ImageNet, a large scale collectio
Externí odkaz:
http://arxiv.org/abs/1705.02139
Classifiers trained on given databases perform poorly when tested on data acquired in different settings. This is explained in domain adaptation through a shift among distributions of the source and target domains. Attempts to align them have traditi
Externí odkaz:
http://arxiv.org/abs/1704.08082