Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

Backpropagation image saliency aims at explaining model predictions by estimating model-
centric importance of individual pixels in the input. However, class-insensitivity of the earlier …

Methods based on class activation maps (CAM) provide a simple mechanism to interpret
predictions of convolutional neural networks by using linear combinations of feature maps …

Deep visual models are susceptible to adversarial perturbations to inputs. Although these
signals are carefully crafted, they still appear noise-like patterns to humans. This observation …

Abstract The capability of Convolutional Neural Networks (CNNs) for sparse representation
has significant application to complex tasks like Representation Learning (RL). However …

Deep neural networks can be fooled by small imperceptible perturbations called adversarial
examples. Although these examples are carefully crafted, they involve two major concerns …

Deep learning has demonstrated state-of-the-art performance for a variety of challenging
computer vision tasks. On one hand, this has enabled deep visual models to pave the way …

Deep visual models are known to be vulnerable to adversarial attacks. The last few years
have seen numerous techniques to compute adversarial inputs for these models. However …

The interpretability of deep neural networks (DNNs) has emerged as a crucial area of
research, particularly in image classification tasks where decisions often lack transparency …