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pro vyhledávání: '"Kulkarni, Tejas D."'
Intrinsic decomposition from a single image is a highly challenging task, due to its inherent ambiguity and the scarcity of training data. In contrast to traditional fully supervised learning approaches, in this paper we propose learning intrinsic im
Externí odkaz:
http://arxiv.org/abs/1711.03678
Autor:
Denil, Misha, Agrawal, Pulkit, Kulkarni, Tejas D, Erez, Tom, Battaglia, Peter, de Freitas, Nando
When encountering novel objects, humans are able to infer a wide range of physical properties such as mass, friction and deformability by interacting with them in a goal driven way. This process of active interaction is in the same spirit as a scient
Externí odkaz:
http://arxiv.org/abs/1611.01843
Learning robust value functions given raw observations and rewards is now possible with model-free and model-based deep reinforcement learning algorithms. There is a third alternative, called Successor Representations (SR), which decomposes the value
Externí odkaz:
http://arxiv.org/abs/1606.02396
Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. The primary difficulty arises due to insufficient exploration, resulting in an agent being unable to learn robust value f
Externí odkaz:
http://arxiv.org/abs/1604.06057
This paper presents the Deep Convolution Inverse Graphics Network (DC-IGN), a model that learns an interpretable representation of images. This representation is disentangled with respect to transformations such as out-of-plane rotations and lighting
Externí odkaz:
http://arxiv.org/abs/1503.03167
Recently, multiple formulations of vision problems as probabilistic inversions of generative models based on computer graphics have been proposed. However, applications to 3D perception from natural images have focused on low-dimensional latent scene
Externí odkaz:
http://arxiv.org/abs/1407.1339
Approximate inference in high-dimensional, discrete probabilistic models is a central problem in computational statistics and machine learning. This paper describes discrete particle variational inference (DPVI), a new approach that combines key stre
Externí odkaz:
http://arxiv.org/abs/1402.5715
The idea of computer vision as the Bayesian inverse problem to computer graphics has a long history and an appealing elegance, but it has proved difficult to directly implement. Instead, most vision tasks are approached via complex bottom-up processi
Externí odkaz:
http://arxiv.org/abs/1307.0060
Akademický článek
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Publikováno v:
2015 7th International Conference on Games & Virtual Worlds for Serious Applications (VS-Games); 2015, p4390-4399, 10p
