CS Colloquium: Alek Kolcz (Chief Scientist at Pushed inc)
Effective deep near-take detection
Deep learning models produce image representations that excel both at semantic and perceptual similarity. Networks trained on the classic ImageNet benchmark are commonly fine-tuned over task-specific data and tend to produce highly accurate solutions for a variety of problems, ranging from image classification to content based search. It has been observed, however, that the deepest architectures such as ResNet, while superior at semantic classification are not as as effective at providing perceptually sensitive representations when compared to older architectures, such as AlexNet or VGG. This has implications for complex solutions for which both of these modalities are important. In this work we investigate the problem of improving the perceptual sensitivity of deeper architectures with application to near-take detection, where the objective is to identify clusters of images with strong visual similarity. We show that with an appropriate modification of the learning objective, deeper architectures such as ResNet substantially improve in their perceptual representations, which maintaining good semantic representations.
Bio: Alek Kolcz is currently the Chief Scientist of Pushd Inc., where he works on a variety Machine Learning and Computer Vision problems. His past research include email/social-media spam detection, document/query classification and clustering, user modeling and personalization at Twitter, Microsoft, AOL and University of Colorado. He holds a PhD in EE from the University of Manchester (former UMIST).
Tuesday, November 20 at 11:00am