Michael Soloveitchik, Michael Werman
We introduce a fully convolutional fractional scaling component, FCFS. Fully convolutional networks can be applied to any size input and previously did not support non-integer scaling. Our architecture is simple with an efficient single layer implementation. Examples and code implementations of three common scaling methods are published.
Michael Soloveitchik, Tzvi Diskin, Efrat Morin et al.
We consider distance functions between conditional distributions. We focus on the Wasserstein metric and its Gaussian case known as the Frechet Inception Distance (FID). We develop conditional versions of these metrics, analyze their relations and provide a closed form solution to the conditional FID (CFID) metric. We numerically compare the metrics in the context of performance evaluation of modern conditional generative models. Our results show the advantages of CFID compared to the classical FID and mean squared error (MSE) measures. In contrast to FID, CFID is useful in identifying failures where realistic outputs which are not related to their inputs are generated. On the other hand, compared to MSE, CFID is useful in identifying failures where a single realistic output is generated even though there is a diverse set of equally probable outputs.
Omer Regev, Michael Soloveitchik, Dror G. Feitelson
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.