SELF-SUPERVISED LEARNING USING IN-PAINTING
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United States of America
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Issued Date -
Nov 28, 2024
app pub date -
May 22, 2023
filing date -
May 22, 2023
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Abstract
Self-supervised learning of a machine learning model using images. The computing device masks a section of each image in an image database to generate a partially masked image. An autoencoder encodes each partially masked image to generate one or more encodings representing each partially masked image. The autoencoder decodes each of the one or more encodings into one or more decoded encodings representing each partially masked image previously input into the autoencoder. The computing device compares each of the one or more decoded encodings of each partially masked image with the corresponding image from the image database to generate an unaugmented model output. The computing device augments each image according to a data augmentation policy to generate an augmented model output. The computing device determines a total loss by comparing the unaugmented model output to the augmented model output. The autoencoder is improved based upon the total loss.
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