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  • Previous activities Current activities

    Overview of what was investigated

    Alexey Gruzdev a.s.gruzdev@yandex.ru

    January 27, 2017

    Alexey Gruzdev Overview of what was investigated

  • Previous activities Current activities

    Agenda

    1 Previous activities Object Detection Semantic Segmentation Style Transfer Diabetic Retinopathy Detection

    2 Current activities Reinforcement Learning Deep Reinforcement Learning

    Alexey Gruzdev Overview of what was investigated

  • Previous activities Current activities

    Object Detection Semantic Segmentation Style Transfer Diabetic Retinopathy Detection

    Object Detection problem formulation

    Input: RGB or grayscale image

    Task:

    find all existing objects on the image

    for each object, calculate ”accurate” bounding box

    Alexey Gruzdev Overview of what was investigated

  • Previous activities Current activities

    Object Detection Semantic Segmentation Style Transfer Diabetic Retinopathy Detection

    Datasets for Object Detection

    PASCAL Visual Object Classes Challenge 2012

    SUN: Large-scale Scene Recognition from Abbey to Zoo

    ImageNet Large Scale Visual Recognition Challenge

    Microsoft Common Objects in Context

    Alexey Gruzdev Overview of what was investigated

    http://host.robots.ox.ac.uk:8080/pascal/VOC/voc2012/ http://vision.princeton.edu/projects/2010/SUN/ http://image-net.org/challenges/LSVRC/2015/ http://mscoco.org/

  • Previous activities Current activities

    Object Detection Semantic Segmentation Style Transfer Diabetic Retinopathy Detection

    Semantic Segmentation problem formulation

    Input: RGB or grayscale image

    Task:

    label each pixel to particular class

    Alexey Gruzdev Overview of what was investigated

  • Previous activities Current activities

    Object Detection Semantic Segmentation Style Transfer Diabetic Retinopathy Detection

    Style Transfer

    Alexey Gruzdev Overview of what was investigated

  • Previous activities Current activities

    Object Detection Semantic Segmentation Style Transfer Diabetic Retinopathy Detection

    Diabetic Retinopathy Detection

    Input data - set of training/testing images. For each training image also known its class (the degree of severity)

    Output data - A clinician has rated the presence of diabetic retinopathy in each image on a scale of 0 to 4, according to the following scale:

    0 - No DR

    1 - Mild

    2 - Moderate

    3 - Severe

    4 - Proliferative DR

    Goal: - create algorithm, which is able to determine the degree of severety for input retina image.

    Alexey Gruzdev Overview of what was investigated

  • Previous activities Current activities

    Object Detection Semantic Segmentation Style Transfer Diabetic Retinopathy Detection

    Diabetic Retinopathy Detection

    Alexey Gruzdev Overview of what was investigated

  • Previous activities Current activities

    Object Detection Semantic Segmentation Style Transfer Diabetic Retinopathy Detection

    Diabetic Retinopathy Detection

    Alexey Gruzdev Overview of what was investigated

  • Previous activities Current activities

    Object Detection Semantic Segmentation Style Transfer Diabetic Retinopathy Detection

    Diabetic Retinopathy Detection

    Alexey Gruzdev Overview of what was investigated

  • Previous activities Current activities

    Object Detection Semantic Segmentation Style Transfer Diabetic Retinopathy Detection

    Diabetic Retinopathy Detection

    Alexey Gruzdev Overview of what was investigated

  • Previous activities Current activities

    Object Detection Semantic Segmentation Style Transfer Diabetic Retinopathy Detection

    Diabetic Retinopathy Detection

    Alexey Gruzdev Overview of what was investigated

  • Previous activities Current activities

    Reinforcement Learning Deep Reinforcement Learning

    Reinforcement Learning

    Alexey Gruzdev Overview of what was investigated

  • Previous activities Current activities

    Reinforcement Learning Deep Reinforcement Learning

    Deep Reinforcement Learning

    Motivation:

    Deep means that at least one function is approximated by Deep Neural Network

    Reinforcement Learning is useful for robotics, continuous control, decision making

    Data is available. Thanks to OpenAI!

    Alexey Gruzdev Overview of what was investigated

  • Previous activities Current activities

    Reinforcement Learning Deep Reinforcement Learning

    Questions?

    Alexey Gruzdev Overview of what was investigated

  • Previous activities Current activities

    Reinforcement Learning Deep Reinforcement Learning

    Alexey Gruzdev Overview of what was investigated

    Previous activities Object Detection Semantic Segmentation Style Transfer Diabetic Retinopathy Detection

    Current activities Reinforcement Learning Deep Reinforcement Learning