Understanding deep neural networks with clustering analysis and structural causal model
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Examensarbete för masterexamen
Programme
Model builders
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Abstract
The blackbox problem of machine learning algorithms has been limiting human trust
in deep neural networks’ decisions. While some researchers use clustering analysis
to enhance networks’ transparency, some use attribution method to improve the
interpretability. This raises a question "how it will be if we apply both approaches
on a deep neural network at once". The current study is an exploratory study that
investigates the possibilities of relieving the blackbox problem by combining clustering
analysis and a structural causal modelling-based attribution method. The study
is developed on a conditional β variational autoencoder (β-VAE). It estimates the
average causal effects (ACEs) of the decoder’s inputs on the hidden layers and reconstruction
layer, then conducts ACE-based and activation-based clustering analysis.
We apply lesion test experiments to identify the clusters that are important to image
reconstruction and answer our question "if there is a non-empty intersection of the
two important clusters that contains neurons which are critical to image reconstruction".
The results show that 1) there is one input neuron carrying the rotation and
scaling roles in image reconstruction, 2) this neuron has positive ACEs on the stroke
of the targeted digit, 3) both of the activation-based and ACE-based clustering analysis
give us at least 5 clusters, 4) there is always one cluster that is important to
image reconstruction, 5) the intersection of the two important clusters is not empty
and contains neurons that are critical to image reconstruction and 6) among those
critical neurons, there are only 4 to 21 hidden neurons in contrast to our decoder
which has 896 hidden neurons. The findings suggest that there may exist the measure
of chance of reducing the cost of training deep neural networks and protecting
networks from being hacked by focusing on the critical hidden neurons.
Description
Keywords
neural networks, conditional variational autoencoder, clustering, structural causal models