Improving Neural Networks Robustness for Computer Vision
Faris Kateb · Digital Collections of Colorado (Colorado State University) · 2018
Image classification had been a challenging task for computers until neural networks reinvented and achieved the state-of-the-art in a competition in 2012.This has changed the research direction of the computer vision tasks and many other fields.However, with all the success of neural networks in image classification, state-of-the-art networks are still vulnerable to major challenges.The first challenge is classifying blurred images, which can be caused when a camera is unfocused, during motion or using image processing filters.The second challenge is classifying affinely transformed images such as rotation, shitting, or scaling.This problem is neglected because the effects of transformation are not obvious as much as other problems.The third challenge is known as adversarial examples which are images that include structured gradient, could be shown as noise for humans or not shown at all, generated from neural networks.The challenge with adversarial images is that they are imperceptible and misclassified with higher confidence rate.This dissertation introduces methods to make neural networks more robust to these challenges and using a new architecture.One of the architecture is named a Simultaneous Convolutional Neural Network (SCNN) that aims to make a model more robust by train a pair of sub-networks.The sub-networks produce learning for original and perturbed iii Semwal, Dr.