  It has been shown that the activations invoked by an image within the top
layers of a large convolutional neural network provide a high-level descriptor
of the visual content of the image. In this paper, we investigate the use of
such descriptors (neural codes) within the image retrieval application. In the
experiments with several standard retrieval benchmarks, we establish that
neural codes perform competitively even when the convolutional neural network
has been trained for an unrelated classification task (e.g.\ Image-Net). We
also evaluate the improvement in the retrieval performance of neural codes,
when the network is retrained on a dataset of images that are similar to images
encountered at test time.
  We further evaluate the performance of the compressed neural codes and show
that a simple PCA compression provides very good short codes that give
state-of-the-art accuracy on a number of datasets. In general, neural codes
turn out to be much more resilient to such compression in comparison other
state-of-the-art descriptors. Finally, we show that discriminative
dimensionality reduction trained on a dataset of pairs of matched photographs
improves the performance of PCA-compressed neural codes even further. Overall,
our quantitative experiments demonstrate the promise of neural codes as visual
descriptors for image retrieval.
