Using Split-Brain autoencoders to learn an unsupervised representation of visual saliency in scenes

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This research focuses on unsupervised methods of predicting the visual saliency of regions of scenes. We use a split brain auto-encoder model in which two auto-encoders - each representing a 'hemisphere' - are trained to predict the output of the other. This is an extension of the predictive processing model of cognition. According to our model, each hemisphere is engaged in attempting to predict the output of the other and in order to do so, must learn useful representations of the input.If successful, this research will provide a useful unsupervised method of predicting visual saliency in scenes, as well as improve our understanding of how the brain is able to extract information out of the jumble of input it receives, as well as the importance of hemisphericity as an organising principle of the brain.Project milestones are as follows: 1.) Conduct a literature review and identify relevant work and similar approaches. 2.) Create a working prototype system3.) Compare the performance of the system on eye-tracking data with maps of visual saliency across scenes, for example the MIT dataset.4.) Iterate and improve the model and report on findings.