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Download fileWhat You See Is What You Transform: Foveated Spatial Transformers as a bio-inspired attention mechanism
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posted on 2021-09-07, 21:28 authored by Ghassan DabaneGhassan Dabane, Laurent PerrinetLaurent Perrinet, Emmanuel DaucéConvolutional 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.
History
Email Address of Submitting Author
dabane.ghassan@gmail.comORCID of Submitting Author
0000-0001-9686-8047Submitting Author's Institution
Institut de Neurosciences de la TimoneSubmitting Author's Country
- France