Architectures for big scale 2D imagery.

I will present research that I conducted during my Ph.D. at University College London and in collaboration with Google. My primary interest lays in the development of neural architectures for 2D imagery problems in big scale. Will present the recently published analysis of different upsampling methods in the decoder part of visual architectures, together with last week ongoing extension for GANs. Will discuss attention mechanism for text recognition and review for what kind of application it can be useful (automatically updating Google Maps based on Google Street View imagery). I will explain the idea behind Inception and what had we change in inception-v3 to have it the best single model on ImageNet 2015 and how does it compare to Resnet architecture which was published 2 weeks after. Together with inception, will present our winning submission to MS COCO 2016 detection challenge and the extensive analysis of different models and backbone architectures inside. At the end will shortly review our UCL effort working with 4096x4096 images at The Digital Mammography DREAM Challenge for breast cancer recognition, where we have achieved 9th among 1375 teams worldwide and 2nd place in the community phase.