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One-Sided Unsupervised Domain Mapping

Neural Information Processing Systems

In unsupervised domain mapping, the learner is given two unmatched datasets $A$ and $B$. Recent approaches have shown that when learning simultaneously both $G_{AB}$ and the inverse mapping $G_{BA}$, convincing mappings are obtained. This is done by learning a mapping that maintains the distance between a pair of samples. Moreover, good mappings are obtained, even by maintaining the distance between different parts of the same sample before and after mapping. We present experimental results that the new method not only allows for one sided mapping learning, but also leads to preferable numerical results over the existing circularity-based constraint.


[R] One-Sided Unsupervised Domain Mapping • r/MachineLearning

@machinelearnbot

In unsupervised domain mapping, the learner is given two unmatched datasets A and B. The goal is to learn a mapping that translates a sample in A to the analog sample in B. Recently CycleGAN and DiscoGAN have shown that when learning simultaneously a mapping from A to B and the inverse mapping from B to A, convincing mappings are obtained. In this work, we present a method of learning a mapping from A to B without the inverse mapping. This is done by learning a mapping that maintains the distance between a pair of samples. Feel free to ask questions.


Mapping Irma, but not really…

@machinelearnbot

We're discussing data visualization nowadays in my course, and today's topic was supposed to be mapping. However late last night I realized I was going to run out of time and decided to table hands on mapping exercises till a bit later in the course (after we do some data manipulation as well, which I think will work better). That being said, talking about maps seemed timely, especially with Hurricane Irma developing. In addition to what's on the slide I told the students that they can assume the map is given, and they should only think about how the forecast lines would be drawn. Everyone came up with "we need latitude and longitude and time".


Closed-Form Inversion of Backpropagation Networks: Theory and Optimization Issues

Neural Information Processing Systems

We describe a closed-form technique for mapping the output of a trained backpropagation network int.o input activity space. The mapping is an inverse mappingin the sense that, when the image of the mapping in input activity space is propagat.ed


Closed-Form Inversion of Backpropagation Networks: Theory and Optimization Issues

Neural Information Processing Systems

We describe a closed-form technique for mapping the output of a trained backpropagation network int.o input activity space. The mapping is an verse in mapping in the sense that, when the image of the mapping in input activity space is propagat.ed