Two-Step Disentanglement for Financial Data
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In this work, we address the problem of disentanglement of factors that generate a given data into those that are correlated with the labeling and those that are not. Our solution is simpler than previous solutions and employs adversarial training in a straightforward manner. We demonstrate the new method on visual datasets as well as on financial data. In order to evaluate the latter, we developed a hypothetical trading strategy whose performance is affected by the performance of the disentanglement, namely, it trades better when the factors are better separated.
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