We consider distance functions between conditional distributions functions. We focus on the Wasserstein metric and its Gaussian case known as the Frechet Inception Distance (FID). We develop conditional versions of these metrics, and analyze their relations. Then, we numerically compare the metrics in the context of performance evaluation of conditional generative models. Our results show that the metrics are similar in classical models which are less susceptible to conditional collapse. But the conditional distances are more informative in modern unsupervised, semisupervised and unpaired models where learning the relations between the inputs and outputs is the main challenge.
Variable and function names are extremely important for program comprehension. It is therefore also important to study how developers select names. But controlled experiments on naming are hindered by the need to describe to experimental subjects what it is they need to name. Words appearing in these descriptions may then find their way into the names, leading to a bias in the results. We suggest that this problem can be alleviated by using emojis or other small graphics in lieu of key words in the descriptions. A replication of previous work on naming, this time including such emojis and graphics, indeed led to a more diverse and less biased choice of words in the names than when using English descriptions.
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