The successful operation of sixty miles of street car lines by the city of San Francisco has long been a favorite citation of the advocates of municipal operation. The enterprize is beginning to borrow pay roll money from its depreciation fund and is evidently not immune from the prevailing malady of high costs.
Infinite patience plus tact and good judgment bring successful cooperation of property owners in replatting two areas where bad planning was marring the city's development. A suggestion for other cities.
Isolating and controlling specific features in the outputs of generative models in a user-friendly way is a difficult and open-ended problem. We develop techniques that allow an oracle user to generate an image they are envisioning in their head by answering a sequence of relative queries of the form "do you prefer image a or image b?" Our framework consists of a Conditional VAE that uses the collected relative queries to partition the latent space into preference-relevant features and non-preference-relevant features. We then use the user's responses to relative queries to determine the preference-relevant features that correspond to their envisioned output image. Additionally, we develop techniques for modeling the uncertainty in images' predicted preference-relevant features, allowing our framework to generalize to scenarios in which the relative query training set contains noise. 1
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