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There are many applications of U-Net in biomedical image segmentation, such as brain image segmentation (''BRATS'' [8]) and liver image segmentation ("siliver07" [9]) as well as protein binding site prediction. [10] U-Net implementations have also found use in the physical sciences, for example in the analysis of micrographs of materials.
An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.
The prediction is made from an earlier picture, mainly an I-frame or P-frame, so that require less coding data (≈50% when compared to I-frame size). The amount of data needed for doing this prediction consist of motion vectors and transform coefficients describing prediction correction. It involves the use of motion compensation.
The ImageNet project is a large visual database designed for use in visual object recognition software research. More than 14 million [1] [2] images have been hand-annotated by the project to indicate what objects are pictured and in at least one million of the images, bounding boxes are also provided. [3]
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A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
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