Deep learning has been quite popular for image recognition and classification tasks in recent years due to its high performances. However, traditional deep learning approaches usually require a large dataset for the model to be trained on to distinguish very few different classes, which is drastically different from how humans are able to learn from even very few examples. Few-shot or one-shot learning is a categorization problem that aims to classify objects given only a limited amount of samples, with the ultimate goal of creating a more human-like learning algorithm. In this article, we will dive into the deep learning approaches to solving the one-shot learning problem by using a special network structure: Siamese Network. We will build the network using PyTorch and test it on the Omniglot handwritten character dataset and performed several experiments to compare the results of different network structures and hyperparameters, using a one-shot learning evaluation metric.

What data actually matters for appraising a property? There is a long list of things to consider, this is a complicated process for humans and not much has changed with the process for decades. Something that human appraisers have struggled to consider are all of the unstructured elements on the property. Many of these topics have been too "subjective" for influence on your price estimate. Sure, if there are gross quality issues (damaged flooring, etc..) that can go into it, but your choice in tile for the backsplash?