Bing Yu (COMP8755)Supervisor: Jo Plested, Tom Gedeon30 July 2018

Generating Varied Object Shapes and Subcategories With Generative Deep Learning Models

Introduction

• What’s the problem?– Shape conditioned image generation

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Introduction

• Motivation– In Image Fixing task, available information is limited– Many previous works on conditional image generation – But using shape as condition is rare

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Background

• GAN• Variational Autoencoder• PixelCNN

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Approach

• PixelCNN

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Approach

• Shape conditioned PixelCNN

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Shape Vector

Conditional PixelCNN

Preliminary Result

• Dataset selection and preprocessing• On polar bear dataset

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Preliminary Result

• Reason and fixing approach– Lack of stochastic in training

• Changing the loss function from MSE to discretized logistic mixture likelihood

– The training image size is too large• Using progressive mode

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RoadmapWeeks Task1 Proving the availability of small dataset on model2~5 Build the progressive growing model6 Running different dataset on modelMidterm Break Finding the baseline and reasonable evaluation method7 Evaluating the model8~10 Modifying and improving11 Writing report, final presentation12 Finishing the report

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Reference:Mr. P Lick Cute Coffee Mug - Best Coffee Mugs http://www.mugs.coffee/coffee-mugs/mr-p-lick-cute-coffee-mug/Viv Champagne Glass https://www.crateandbarrel.com/viv-champagne-glass/s240753Oord, A. V. D., Kalchbrenner, N., & Kavukcuoglu, K. (2016). Pixel recurrent neural networks. arXivpreprint arXiv:1601.06759.

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