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Size inconsistency has existed since at least 1937. In Sears' 1937 catalog, a size 14 dress had a bust size of 32 inches (81 cm). In 1967, the same bust size was a size 8. In 2011, it was a size 0. [7] Some argue that vanity sizing is designed to satisfy wearers' wishes to appear thin and feel better about themselves.
The product should not be labelled with the average body dimension for which the garment was designed (i.e., not "height: 176 cm."). Instead, the label should show the range of body dimensions from half the step size below to half the step size above the design size (e.g., "height: 172–180 cm.").
It is currently in common use for children's clothing, but not yet for adults. The third standard EN 13402-3 seeks to address the problem of irregular or vanity sizing through offering a SI unit based labelling system, which will also pictographically describe the dimensions a garment is designed to fit, per the ISO 3635 standard.
There are multiple size types, designed to fit somewhat different body shapes. Variations include the height of the person's torso (known as back length), whether the bust, waist, and hips are straighter (characteristic of teenagers) or curvier (like many adult women), and whether the bust is higher or lower (characteristic of younger and older women, respectively).
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Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
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