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CutOutRL - Visualizing Neural Networks with Scribbles

Yonatan Zarecki ,Ziv Izhar

Supervised by Elad Richardson


Deep neural networks (DNNs) have been very successful in recent years, achieving state-of-the-art results in a wide range of domains, such as voice recognition, image segmentation, face recognition and more. In addition, reinforcement-learning (RL) training methods combined with DNN models (deep RL) have been able to solve a wide variety of games, from PONG to Mario, purely by looking at the pixel values of the screen.

Various “games” have been proposed for challenging neural networks, testing their capacity to learn complex tasks. Some tasks are designed to give us human insight about the way the model operates.

In this project, we challenged a deep RL model with the task of segmenting an image using scribbles. We force it to achieve good segmentations by using scribble-based segmentation in a way similar to humans. We hope to gain insight on the way the network does segmentation by looking at the scribbles it generates.

Project CutOutRL - Visualizing Neural Networks with Scribbles Picture 1
Project CutOutRL - Visualizing Neural Networks with Scribbles Picture 3
Project Report

Please, see project report.

Final Presentation

Please, see final presentation.

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