Title: Cost-Effective Training of Deep CNNs with Active Model Adaptation,
Abstract: Deep convolutional neural networks have achieved great success in various
applications. However, training an effective DNN model for a specific task is
rather challenging because it requires a prior knowledge or experience to
design the network architecture, repeated trial-and-error process to tune the
parameters, and a large set of labeled data to train the model. In this paper,
we propose to overcome these challenges by actively adapting a pre-trained
model to a new task with less labeled examples. Specifically, the pre-trained
model is iteratively fine tuned based on the most useful examples. The examples
are actively selected based on a novel criterion, which jointly estimates the
potential contribution of an instance on optimizing the feature representation
as well as improving the classification model for the target task. On one hand,
the pre-trained model brings plentiful information from its original task,
avoiding redesign of the network architecture or training from scratch; and on
the other hand, the labeling cost can be significantly reduced by active label
querying. Experiments on multiple datasets and different pre-trained models
demonstrate that the proposed approach can achieve cost-effective training of
DNNs.