- "We specifically consider the case of...a stacked deep autoencoder (AE), which is a type of neural network designed to encode a set of data samples such that they can be decoded to produce data sample reconstructions with minimal error
- "The first step of the NDL algorithm occurs when a set of new data points fail to be appropriately reconstructed by the trained network...When a data sample’s RE is too high, the assumption is that the AE level under examination does not contain a rich enough set of features to accurately reconstruct the sample.
- "The second step of the NDL algorithm is adding and training a new node, which occurs when a critical number of input data samples (outliers) fail to achieve adequate representation at some level of the network.
- "The final step of the NDL algorithm is intended to stabilize the network’s previous representations in the presence of newly added nodes. It involves training all the nodes in a level with both new data and replayed samples from previously seen classes on which the network has been trained.
- "We specifically consider the case of...a stacked deep autoencoder (AE), which is a type of neural network designed to encode a set of data samples such that they can be decoded to produce data sample reconstructions with minimal error
- "The first step of the NDL algorithm occurs when a set of new data points fail to be appropriately reconstructed by the trained network...When a data sample’s RE is too high, the assumption is that the AE level under examination does not contain a rich enough set of features to accurately reconstruct the sample.
- "The second step of the NDL algorithm is adding and training a new node, which occurs when a critical number of input data samples (outliers) fail to achieve adequate representation at some level of the network.
- "The final step of the NDL algorithm is intended to stabilize the network’s previous representations in the presence of newly added nodes. It involves training all the nodes in a level with both new data and replayed samples from previously seen classes on which the network has been trained.