The past decade has witnessed the great success of deep learning in many disciplines,
especially in computer vision and image processing. However, deep learning-based video …

Deep learning architectures are now pervasive and filled almost all applications under
image processing, computer vision, and biometrics. The attractive property of feature …

We systematically study a wide variety of generative models spanning semantically-diverse
image datasets to understand and improve the feature extractors and metrics used to …

Numerous task-specific variants of conditional generative adversarial networks have been
developed for image completion. Yet, a serious limitation remains that all existing algorithms …

Convolutional autoencoders have emerged as popular methods for unsupervised defect
segmentation on image data. Most commonly, this task is performed by thresholding a pixel …

Generative models, in particular generative adversarial networks (GANs), have gained
significant attention in recent years. A number of GAN variants have been proposed and …

Neural networks are vulnerable to adversarial examples, which poses a threat to their
application in security sensitive systems. We propose high-level representation guided …

Recent advances in deep learning have shown exciting promise in filling large holes in
natural images with semantically plausible and context aware details, impacting …

We present an autoencoder that leverages learned representations to better measure
similarities in data space. By combining a variational autoencoder (VAE) with a generative …

The performance of objective image quality assessment (IQA) models has been evaluated
primarily by comparing model predictions to human quality judgments. Perceptual datasets …