  As of today, object categorization algorithms are not able to achieve the
level of robustness and generality necessary to work reliably in the real
world. Even the most powerful convolutional neural network we can train fails
to perform satisfactorily when trained and tested on data from different
databases. This issue, known as domain adaptation and/or dataset bias in the
literature, is due to a distribution mismatch between data collections. Methods
addressing it go from max-margin classifiers to learning how to modify the
features and obtain a more robust representation. Recent work showed that by
casting the problem into the image-to-class recognition framework, the domain
adaptation problem is significantly alleviated \cite{danbnn}. Here we follow
this approach, and show how a very simple, learning free Naive Bayes Nearest
Neighbor (NBNN)-based domain adaptation algorithm can significantly alleviate
the distribution mismatch among source and target data, especially when the
number of classes and the number of sources grow. Experiments on standard
benchmarks used in the literature show that our approach (a) is competitive
with the current state of the art on small scale problems, and (b) achieves the
current state of the art as the number of classes and sources grows, with
minimal computational requirements.
