Zobrazeno 1 - 10
of 82
pro vyhledávání: '"Puşcaş, Mihai"'
Autor:
Puscas, Mihai
The use of Deep Neural Networks with their increased representational power has allowed for great progress in core areas of computer vision, and in their applications to our day-to-day life. Unfortunately the performance of these systems rests on the
Externí odkaz:
https://hdl.handle.net/11572/368728
Autor:
Puscas, Mihai
Les pelouses alpines à Carex curvula (laîche courbée) constituent l'une des formations les plus emblématiques de l'étage alpin des montagnes européennes. Cette thèse se présente comme une contribution à l'étude des patrons de la diversité
Externí odkaz:
http://tel.archives-ouvertes.fr/tel-00386998
http://tel.archives-ouvertes.fr/docs/00/38/69/98/PDF/Puscas_these.pdf
http://tel.archives-ouvertes.fr/docs/00/38/69/98/PDF/Puscas_these.pdf
Although providing exceptional results for many computer vision tasks, state-of-the-art deep learning algorithms catastrophically struggle in low data scenarios. However, if data in additional modalities exist (e.g. text) this can compensate for the
Externí odkaz:
http://arxiv.org/abs/2011.08899
Autor:
Pilzer, Andrea, Lathuilière, Stéphane, Xu, Dan, Puscas, Mihai Marian, Ricci, Elisa, Sebe, Nicu
Recent deep monocular depth estimation approaches based on supervised regression have achieved remarkable performance. However, they require costly ground truth annotations during training. To cope with this issue, in this paper we present a novel un
Externí odkaz:
http://arxiv.org/abs/1909.07667
Inspired by the success of adversarial learning, we propose a new end-to-end unsupervised deep learning framework for monocular depth estimation consisting of two Generative Adversarial Networks (GAN), deeply coupled with a structured Conditional Ran
Externí odkaz:
http://arxiv.org/abs/1908.05794
Models trained in the context of continual learning (CL) should be able to learn from a stream of data over an undefined period of time. The main challenges herein are: 1) maintaining old knowledge while simultaneously benefiting from it when learnin
Externí odkaz:
http://arxiv.org/abs/1904.03137
Since the advent of deep learning, neural networks have demonstrated remarkable results in many visual recognition tasks, constantly pushing the limits. However, the state-of-the-art approaches are largely unsuitable in scarce data regimes. To addres
Externí odkaz:
http://arxiv.org/abs/1901.01868
While recent deep monocular depth estimation approaches based on supervised regression have achieved remarkable performance, costly ground truth annotations are required during training. To cope with this issue, in this paper we present a novel unsup
Externí odkaz:
http://arxiv.org/abs/1807.10915
Autor:
Hurdu, Bogdan-Iuliu, Coste, Ana, Halmagyi, Adela, Szatmari, Paul-Marian, Farkas, Anca, Pușcaș, Mihai, Dan Turtureanu, Pavel, Roșca-Casian, Oana, Tănase, Cătălin, Oprea, Adrian, Mardari, Constantin, Răduțoiu, Daniel, Camen-Comănescu, Petronela, Sîrbu, Ioana-Minodora, Stoie, Andrei, Lupoae, Paul, Cristea, Victoria, Jarda, Liliana, Holobiuc, Irina, Goia, Irina, Cătană, Corina, Butiuc-Keul, Anca
Publikováno v:
In Journal for Nature Conservation August 2022 68
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