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A deep autoencoder is trained in the same way as a single-layer neural network. It is composed of two, symmetrical deep-belief networks. One network for encoding and another for decoding.
The encoder layer compresses the input data while the decoder layer does the reverse to produce the uncompressed version of the data.
The actual use of autoencoders is for determining a compressed version of the input data with the lowest amount of loss in data. Important data is not lost but the overall size of the data is reduced significantly. |
IEEE/ICACT20210175 Slide.25
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