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What You See Is What You Transform: Foveated Spatial Transformers as a bio-inspired attention mechanism
  • Ghassan Dabane ,
  • Laurent Perrinet ,
  • Emmanuel Daucé
Ghassan Dabane
Institut de Neurosciences de la Timone

Corresponding Author:[email protected]

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Laurent Perrinet
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Emmanuel Daucé
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Convolutional Neural Networks have been considered the go-to option for object recognition in computer vision for the last couple of years. However, their invariance to object’s translations is still deemed as a weak point and remains limited to small translations only via their max-pooling layers. One bio-inspired approach considers the What/Where pathway separation in Mammals to overcome this limitation. This approach works as a nature-inspired attention mechanism, another classical approach of which is Spatial Transformers. These allow an adaptive endto-end learning of different classes of spatial transformations throughout training. In this work, we overview Spatial Transformers as an attention-only mechanism and compare them with the What/Where model. We show that the use of attention restricted or “Foveated” Spatial Transformer Networks, coupled alongside a curriculum learning training scheme and an efficient log-polar visual space entry, provides better performance when compared to the What/Where model, all this without the need for any extra supervision whatsoever.