  In recent years, convolutional neural networks (CNNs) have been applied
successfully in many fields. However, such deep neural models are still
regarded as black box in most tasks. One of the fundamental issues underlying
this problem is understanding which features are most influential in image
recognition tasks and how they are processed by CNNs. It is widely accepted
that CNN models combine low-level features to form complex shapes until the
object can be readily classified, however, several recent studies have argued
that texture features are more important than other features. In this paper, we
assume that the importance of certain features varies depending on specific
tasks, i.e., specific tasks exhibit a feature bias. We designed two
classification tasks based on human intuition to train deep neural models to
identify anticipated biases. We devised experiments comprising many tasks to
test these biases for the ResNet and DenseNet models. From the results, we
conclude that (1) the combined effect of certain features is typically far more
influential than any single feature; (2) in different tasks, neural models can
perform different biases, that is, we can design a specific task to make a
neural model biased toward a specific anticipated feature.
