Object classi cation under the predictive processing approach
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2021-06-18
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en
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Abstract
Current state-of-the-art algorithms for visual object classi cation are
based on training large convolutional arti cial neural networks on labeled
data sets. This means that rst, the features are directly learned for object
classi cation and the computation is performed using the forward pass of
the network. Conversely, evidence in computational neuroscience reveals
recurrency, top-down modulation and unsupervised learning in the visual
cortex. Here, I study the potential of the predictive processing approach in
classi cation tasks. First, I compared the classi cation performance of unsupervised
learnt features (using a variational autoencoder) against standard
supervised approaches (VGG). Second, I studied the advantages of modeling
the classi cation task as an inference process, with bottom-up and
top-down modulation. While using predictive processing for classi cation
on the CIFAR-10 data set, was shown to be inferior to that of the VGG
model that is trained directly on classi cation error in a supervised manner
with 56.71% against 84.23% classi cation accuracy, the classi cation accuracy
of 98.63% on the MNIST data set showed there is de nitely potential.
By modeling the classi cation task as an inference process, with bottom-up
and top-down modulation, I showed that this method can help with the
reduction of uncertainty that is present in the real world.
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Faculteit der Sociale Wetenschappen