Engineering Commons^™

No Room For Squares: Using Bitmap Masks To Improve Pedestrian Detection Using Cnns., Adam Warde, Hamza Yous, David Gregg, David Moloney

Session 3: Deep Learning for Computer Vision

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In this paper we investigate a method to reduce the number of computations and associated activations in Convolutional Neural Networks (CNN) by using bitmaps. The bitmaps are used to mask the input images to the network that fall within a rectangular window but do not fall within the boundaries of the objects the network is being trained upon. The mask has the effect of rendering the operations on these portions of the training images trivial. The thesis is that applying this approach to CNNs will not degrade accuracy while at the same time reducing the computational workload and reducing …

Improving Unsupervised Learning With Exemplar Cnns, Eric Arazo, Noel E. O'Connor, Kevin Mcguinness

Session 3: Deep Learning for Computer Vision

Most recent unsupervised learning methods explore alternative objectives, often referred to as self-supervised tasks, to train convolutional neural networks without the supervision of human annotated labels. This paper explores the generation of surrogate classes as a self-supervised alternative to learn discriminative features, and proposes a clustering algorithm to overcome one of the main limitations of this kind of approach. Our clustering technique improves the initial implementation and achieves 76.4% accuracy in the STL-10 test set, surpassing the current state-ofthe- art for the STL-10 unsupervised benchmark. We also explore several issues with the unlabeled set from STL-10 that should be considered …