Learning Invariances for Interpretability using Supervised VAE
- URL: http://arxiv.org/abs/2007.07591v1
- Date: Wed, 15 Jul 2020 10:14:16 GMT
- Title: Learning Invariances for Interpretability using Supervised VAE
- Authors: An-phi Nguyen, Mar\'ia Rodr\'iguez Mart\'inez
- Abstract summary: We learn model invariances as a means of interpreting a model.
We propose a supervised form of variational auto-encoders (VAEs)
We show how combining our model with feature attribution methods it is possible to reach a more fine-grained understanding about the decision process of the model.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We propose to learn model invariances as a means of interpreting a model.
This is motivated by a reverse engineering principle. If we understand a
problem, we may introduce inductive biases in our model in the form of
invariances. Conversely, when interpreting a complex supervised model, we can
study its invariances to understand how that model solves a problem. To this
end we propose a supervised form of variational auto-encoders (VAEs).
Crucially, only a subset of the dimensions in the latent space contributes to
the supervised task, allowing the remaining dimensions to act as nuisance
parameters. By sampling solely the nuisance dimensions, we are able to generate
samples that have undergone transformations that leave the classification
unchanged, revealing the invariances of the model. Our experimental results
show the capability of our proposed model both in terms of classification, and
generation of invariantly transformed samples. Finally we show how combining
our model with feature attribution methods it is possible to reach a more
fine-grained understanding about the decision process of the model.
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