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A Condition Number for Joint Optimization of Cycle-Consistent Networks
Leonidas Guibas · Qixing Huang · Zhenxiao Liang

Wed Dec 11 10:20 AM -- 10:25 AM (PST) @ West Exhibition Hall C + B3

A recent trend in optimizing maps such as dense correspondences between objects or neural networks between pairs of domains is to optimize them jointly. In this context, there is a natural \textsl{cycle-consistency} constraint, which regularizes composite maps associated with cycles, i.e., they are forced to be identity maps. However, as there is an exponential number of cycles in a graph, how to sample a subset of cycles becomes critical for efficient and effective enforcement of the cycle-consistency constraint. This paper presents an algorithm that select a subset of weighted cycles to minimize a condition number of the induced joint optimization problem. Experimental results on benchmark datasets justify the effectiveness of our approach for optimizing dense correspondences between 3D shapes and neural networks for predicting dense image flows.

Author Information

Leonidas Guibas (stanford.edu)
Qixing Huang (The University of Texas at Austin)
Zhenxiao Liang (The University of Texas at Austin)

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