Statistical transformer networks: learning shape and appearance models via self supervision

7 Apr 2018 Anil Bas William A. P. Smith

We generalise Spatial Transformer Networks (STN) by replacing the parametric transformation of a fixed, regular sampling grid with a deformable, statistical shape model which is itself learnt. We call this a Statistical Transformer Network (StaTN)... (read more)

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Methods used in the Paper


METHOD TYPE
Spatial Transformer
Image Model Blocks
Residual Connection
Skip Connections
BPE
Subword Segmentation
Dense Connections
Feedforward Networks
Label Smoothing
Regularization
ReLU
Activation Functions
Adam
Stochastic Optimization
Softmax
Output Functions
Dropout
Regularization
Multi-Head Attention
Attention Modules
Layer Normalization
Normalization
Scaled Dot-Product Attention
Attention Mechanisms
Transformer
Transformers