Generative and Discriminative Voxel Modeling

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Presentation transcript:

Generative and Discriminative Voxel Modeling Andrew Brock

Introduction Choice of representation is key!

Background: VoxNet Maturana et al. 2015

Background: VAEs

Background: VAEs

VAE Architecture

Reconstruction Objective Standard Binary Cross-Entropy Modified Binary Cross-Entropy

Error Surface

Reconstruction Objective

Reconstruction Results

Samples and Interpolations

Interface

Classification: Prior Art

Classification: Low-Hanging Fruit All previous works only considered relatively shallow volumetric ConvNets (or non-volumetric ConvNets).

Classification: Low-Hanging Fruit All previous works only considered relatively shallow volumetric ConvNets (or non-volumetric ConvNets). Utterly unsurprisingly, deeper nets perform much better.

Classification: Low-Hanging Fruit All previous works only considered relatively shallow volumetric ConvNets (or non-volumetric ConvNets). Utterly unsurprisingly, deeper nets perform much better. But, that doesn’t mean we have to be naïve!

Voxception

Voxception-ResNet

Voxception-ResNet

Data and Training -Use ELUs, Batch-Norm, and pre-activation -Change the binary voxel range to {-1,5} to encourage the network to pay more attention to positive entries (and to improve its ability to learn about negative entries) -Warm up on 12 rotated-instance set (12 epochs) then anneal fine-tune on 24 rotated-instances.

Orthogonal Regularization Initializing weights with orthogonal matrices works well…so why not keep them orthogonal?

Results

Results …but don’t pay too much attention to the numbers

Thanks!