The Effect of Blur on Convolutional Networks

I've been working on exploring the impact of blur on convolutional networks with Ayan Chakrabarti and Greg Shakhnarovich at TTIC.

We find that the accuracy of pre-trained classification and semantic segmentation networks drops when they are evaluated on blurred versions of the test set. We explored various fine-tuning strategies, augmenting the dataset with blurred images (with increasing levels of blur, e.g. defocus blur kernel with radius r=2,4,6,8) and find that we can substantially improve accuracy on blurred images without having a large effect on the original sharp images.


Zoomout Semantic Segmentation

I am a contributor to the Zoomout semantic segmentation project with Reza Mostajabi at TTI-Chicago. Zoomout (also called "hypercolumns") is a technique for creating a model for pixelwise labeling through the use of a deep pretrained image classifier.