TY - GEN
T1 - Faster attend-infer-repeat with tractable probabilistic models
AU - Stelzner, Karl
AU - Peharz, Robert
AU - Kersting, Kristian
PY - 2019/1/1
Y1 - 2019/1/1
N2 - The recent Attend-Infer-Repeat (AIR) framework marks a milestone in structured probabilistic modeling, as it tackles the challenging problem of unsupcrviscd scene understanding via Baycsian inference. AIR expresses the composition of visual scenes from individual objects, and uses vari-ational autoencoders to model the appearance of those objects. However, inference in the overall model is highly intractable, which hampers its learning speed and makes it prone to suboptimal solutions. In this paper, we show that the speed and robustness of learning in AIR can be considerably improved by replacing the intractable object representations with tractable probabilistic models. In particular, we opt for sum-product networks (SPNs), expressive deep probabilistic models with a rich set of tractable inference routines. The resulting model, called SuPAIR, learns an order of magnitude faster than AIR, treats object occlusions in a consistent manner, and allows for the inclusion of a background noise model, improving the robustness of Bayesian scene understanding.
AB - The recent Attend-Infer-Repeat (AIR) framework marks a milestone in structured probabilistic modeling, as it tackles the challenging problem of unsupcrviscd scene understanding via Baycsian inference. AIR expresses the composition of visual scenes from individual objects, and uses vari-ational autoencoders to model the appearance of those objects. However, inference in the overall model is highly intractable, which hampers its learning speed and makes it prone to suboptimal solutions. In this paper, we show that the speed and robustness of learning in AIR can be considerably improved by replacing the intractable object representations with tractable probabilistic models. In particular, we opt for sum-product networks (SPNs), expressive deep probabilistic models with a rich set of tractable inference routines. The resulting model, called SuPAIR, learns an order of magnitude faster than AIR, treats object occlusions in a consistent manner, and allows for the inclusion of a background noise model, improving the robustness of Bayesian scene understanding.
UR - https://research.tue.nl/en/publications/49c00514-1491-4264-a9f3-e67a51ff1926
M3 - Conference paper
SN - 9781510886988
BT - 36th International Conference on Machine Learning, ICML 2019
ER -