Demonstrating that dataset domains are largely linearly separable in the feature space of common CNNs
Deep convolutional neural networks (DCNNs) have achieved state of the art performance on a variety of tasks. These high-performing networks require large and diverse training datasets to facilitate generalization when extracting high-level features from low-level data. However, even with the availability of these diverse datasets, DCNNs are not prepared to handle all the data that could be thrown at them. One major challenges DCNNs face is the notion of forced choice. For example, a network trained for image classification is configured to choose from a predefined set of labels with the expectation ...
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