  Recognizing objects in natural images is an intricate problem involving
multiple conflicting objectives. Deep convolutional neural networks, trained on
large datasets, achieve convincing results and are currently the
state-of-the-art approach for this task. However, the long time needed to train
such deep networks is a major drawback. We tackled this problem by reusing a
previously trained network. For this purpose, we first trained a deep
convolutional network on the ILSVRC2012 dataset. We then maintained the learned
convolution kernels and only retrained the classification part on different
datasets. Using this approach, we achieved an accuracy of 67.68 % on CIFAR-100,
compared to the previous state-of-the-art result of 65.43 %. Furthermore, our
findings indicate that convolutional networks are able to learn generic feature
extractors that can be used for different tasks.
