In many applications, access to large quantities of labeled data is prohibitive due to its cost or lack of access to classes of interest. This problem is exacerbated in the context of specific subclasses and data types that are not easily accessible, such as remotes sensing data. The problem of limited data for specific classes of data is referred to as the low-shot or few-shot problem. Typically in the low-shot problem, there is a wealth of data from a source domain that is leveraged to train a convolutional feature extractor that is then applied to a target domain in innovative ways. In this work we apply this framework to the low-shot and fully sampled problem, in which the convolutional neural network is used as a feature extractor and paired with an alternate classifier. We evaluate the benefits of this approach in two contexts, a baseline problem, and limited training data. Additionally, we investigate the impact of loss function selection and sequestering of low-shot data on the classification performance of this approach. We present an applications of these techniques on the recent public xView dataset.
G. Steven Goley, Brayden Osborne, Scott Kangas, and Adam R. Nolan, "On generalization of deep learning recognizers in overhead imagery," Proc. SPIE 10988, Automatic Target Recognition XXIX, 109880K (Presented at SPIE Defense + Commercial Sensing: April 16, 2019; Published: 14 May 2019); https://doi.org/10.1117/12.2519064.
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