  This paper proposes a straightforward and cost-effective approach to assess
whether a deep neural network (DNN) relies on the primary concepts of training
samples or simply learns discriminative, yet simple and irrelevant features
that can differentiate between classes. The paper highlights that DNNs, as
discriminative classifiers, often find the simplest features to discriminate
between classes, leading to a potential bias towards irrelevant features and
sometimes missing generalization. While a generalization test is one way to
evaluate a trained model's performance, it can be costly and may not cover all
scenarios to ensure that the model has learned the primary concepts.
Furthermore, even after conducting a generalization test, identifying bias in
the model may not be possible. Here, the paper proposes a method that involves
recovering samples from the parameters of the trained model and analyzing the
reconstruction quality. We believe that if the model's weights are optimized to
discriminate based on some features, these features will be reflected in the
reconstructed samples. If the recovered samples contain the primary concepts of
the training data, it can be concluded that the model has learned the essential
and determining features. On the other hand, if the recovered samples contain
irrelevant features, it can be concluded that the model is biased towards these
features. The proposed method does not require any test or generalization
samples, only the parameters of the trained model and the training data that
lie on the margin. Our experiments demonstrate that the proposed method can
determine whether the model has learned the desired features of the training
data. The paper highlights that our understanding of how these models work is
limited, and the proposed approach addresses this issue.
