  Conventionally, convolutional neural networks (CNNs) process different images
with the same set of filters. However, the variations in images pose a
challenge to this fashion. In this paper, we propose to generate
sample-specific filters for convolutional layers in the forward pass. Since the
filters are generated on-the-fly, the model becomes more flexible and can
better fit the training data compared to traditional CNNs. In order to obtain
sample-specific features, we extract the intermediate feature maps from an
autoencoder. As filters are usually high dimensional, we propose to learn a set
of coefficients instead of a set of filters. These coefficients are used to
linearly combine the base filters from a filter repository to generate the
final filters for a CNN. The proposed method is evaluated on MNIST, MTFL and
CIFAR10 datasets. Experiment results demonstrate that the classification
accuracy of the baseline model can be improved by using the proposed filter
generation method.
