209 citations · 709 across the 27 of their papers we have counts for
5 papers · 2 filters
Effectively Unbiased FID and Inception Score and where to find them
Min Jin Chong, David Forsyth
This paper shows that two commonly used evaluation metrics for generative models, the Fréchet Inception Distance (FID) and the Inception Score (IS), are biased -- the expected valu…
Improving Style Transfer with Calibrated Metrics
Mao-Chuang Yeh, Shuai Tang, Anand Bhattad +2
Style transfer methods produce a transferred image which is a rendering of a content image in the manner of a style image. We seek to understand how to improve style transfer. To d…
Counterfactual Depth from a Single RGB Image
Theerasit Issaranon, Chuhang Zou, David Forsyth
We describe a method that predicts, from a single RGB image, a depth map that describes the scene when a masked object is removed - we call this "counterfactual depth" that models…
Why do These Match? Explaining the Behavior of Image Similarity Models
Bryan A. Plummer, Mariya I. Vasileva, Vitali Petsiuk +2
Explaining a deep learning model can help users understand its behavior and allow researchers to discern its shortcomings. Recent work has primarily focused on explaining models fo…
Unrestricted Adversarial Examples via Semantic Manipulation
Anand Bhattad, Min Jin Chong, Kaizhao Liang +2
Machine learning models, especially deep neural networks (DNNs), have been shown to be vulnerable against adversarial examples which are carefully crafted samples with a small magn…