  Deep networks have gained immense popularity in Computer Vision and other
fields in the past few years due to their remarkable performance on
recognition/classification tasks surpassing the state-of-the art. One of the
keys to their success lies in the richness of the automatically learned
features. In order to get very good accuracy, one popular option is to increase
the depth of the network. Training such a deep network is however infeasible or
impractical with moderate computational resources and budget. The other
alternative to increase the performance is to learn multiple weak classifiers
and boost their performance using a boosting algorithm or a variant thereof.
But, one of the problems with boosting algorithms is that they require a
re-training of the networks based on the misclassified samples. Motivated by
these problems, in this work we propose an aggregation technique which combines
the output of multiple weak classifiers. We formulate the aggregation problem
using a mixture model fitted to the trained classifier outputs. Our model does
not require any re-training of the `weak' networks and is computationally very
fast (takes $<30$ seconds to run in our experiments). Thus, using a less
expensive training stage and without doing any re-training of networks, we
experimentally demonstrate that it is possible to boost the performance by
$12\%$. Furthermore, we present experiments using hand-crafted features and
improved the classification performance using the proposed aggregation
technique. One of the major advantages of our framework is that our framework
allows one to combine features that are very likely to be of distinct
dimensions since they are extracted using different networks/algorithms. Our
experimental results demonstrate a significant performance gain from the use of
our aggregation technique at a very small computational cost.
