My Talk in  Medico challenge: Generative Adversarial Networks for Automatic Polyp Segmentation 

This paper aims to contribute in bench-marking the automatic polyp segmentation problem using generative adversarial networks framework. Perceiving the problem as an image-to-image translation task, conditional generative adversarial networks are utilized to generate masks conditioned by the images as inputs. Both generator and discriminator are convolution neural networks based. The model achieved 0.4382 on Jaccard index and 0.611 as F2 score. https://multimediaeval.github.io/editions/2020/tasks/medico/ 

My Talk in IEEE SmartGridComm 2020: 
Generative Adversarial Networks & Transfer Learning for Non-Intrusive Load Monitoring in Smart Grids

My Talk in Confer Conference :   An Empirical Analysis of Transfer Learning for Generative Adversarial Networks 

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