  Autoencoders learn data representations through reconstruction. Robust
training is the key factor affecting the quality of the learned representations
and, consequently, the accuracy of the application that use them. Previous
works suggested methods for deciding the optimal autoencoder configuration
which allows for robust training. Nevertheless, improving the accuracy of a
trained autoencoder has got limited, if no, attention. We propose a new
approach that improves the accuracy of a trained autoencoders results and
answers the following question, Given a trained autoencoder, a test image, and
using a real-parameter optimizer, can we generate better quality reconstructed
image version than the one generated by the autoencoder?. Our proposed approach
combines both the decoder part of a trained Resitricted Boltman Machine-based
autoencoder with the Competitive Swarm Optimization algorithm. Experiments show
that it is possible to reconstruct images using trained decoder from randomly
initialized representations. Results also show that our approach reconstructed
better quality images than the autoencoder in most of the test cases.
Indicating that, we can use the approach for improving the performance of a
pre-trained autoencoder if it does not give satisfactory results.
