The eleventh round of the Yelp Dataset Challenge ran throughout the first half of 2018 and we received many impressive, original, and fascinating submissions. As usual, we were struck by the quality of the entries: keep up the good work, folks!
Today, we are proud to announce the grand prize winner of the $5,000 award: “Generalized Latent Variable Recovery for Generative Adversarial Networks” by Nicholas Egan, Jeffrey Zhang, and Kevin Shen (from the Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science). The authors used a Deep Convolutional Generative Adversarial Network (DCGAN) to create photo-realistic pictures of food by training on images from Yelp. Through use of Gaussian priors on the latent variables in the images and other standard technique improvements, they managed to train DCGANs that perform better than past models. Their DCGANs can also be adapted to multiple applications in the industry. The three authors presented at Yelp’s weekly Engineering Learning Group last month to further describe their work and its various utilizations.
This entry was selected from numerous submissions for its technical and academic merit by our panel of data scientists, data mining engineers, and software engineers. For a list of all previous Yelp Dataset winners, head over to the challenge site. Thanks to all who participated!