Single image super-resolution (SISR) deals with a fundamental problem of upsampling a
low-resolution (LR) image to its high-resolution (HR) version. Last few years have witnessed …

Computer vision systems in real-world applications need to be robust to partial occlusion
while also being explainable. In this work, we show that black-box deep convolutional …

Recent work has shown that deep convolutional neural networks (DCNNs) do not
generalize well under partial occlusion. Inspired by the success of compositional models at …

In recent years, Generative Adversarial Networks have become ubiquitous in both research
and public perception, but how GANs convert an unstructured latent code to a high quality …

Using techniques derived from the syntactic methods for visual pattern recognition is not
new and was much explored in the area called syntactical or structural pattern recognition …

Deep convolutional neural networks (DCNNs) are powerful models that yield impressive
results at object classification. However, recent work has shown that they do not generalize …

Technical Debt (TD) has been defined as" code being not quite right yet", and its presence is
often self-admitted by developers through comments. The purpose of such comments is to …

Convolutional neural networks (CNNs) have shown great success in computer vision,
approaching human-level performance when trained for specific tasks via application …

The Gaussian kernel and its derivatives have already been employed for Convolutional
Neural Networks in several previous works. Most of these papers proposed to compute …

Convolutional neural networks excel in a number of computer vision tasks. One of their most
crucial architectural elements is the effective receptive field size, which has to be manually …