Structuring Autoencoders for Sparsely Supervised Labeling
AbstractThe invention concerns a method for the cost-effective and time-efficient labeling and classification of data in neural networks, applicable in information and communication technology, e.g. in the fields of image and data processing, pattern recognition, speech recognition as well as in control engineering.
The generation of labelled data is extremely cost-intensive and error-prone. Using Amazon Mechanical Turk, for example, requires accurate verification of label results, automated relabelling and filtering of useful results.
Innovation / SolutionMore important than the amount of data is the quality of this. Therefore, automated algorithms that can request which data to label are of particular economic interest. The invention "Structuring AutoEncoder" (SAE) is a solution for exactly this problem (among some other aspects). SAEs are neural networks that are trained with a small amount of data and optionally form a desired structure in latent space with predefined labels. There are two inventional loss functions which optimize the reconstruction error on the one hand and the structural loss of the data in the latent space on the other hand. This enables a semantically structured, low-dimensional representation of data.
fields of applicationImage and data processing, pattern recognition, control engineering, speech recognition, machine and plant construction.
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