Abstract: A machine learning method includes: distinguishing foregrounds and backgrounds of a first image to generate a first mask image; cropping the first image to generate second and third images; cropping the first mask image to generate second and third mask images, wherein a position of the second mask image and a position of the third mask image correspond to a position of the second image and a position of the third image, respectively; generating a first feature vector group of the second image and a second feature vector group of the third image by a model; generating a first matrix according to the first and second feature vector groups; generating a second matrix according to the second and third mask images; generating a function according to the first and second matrices; and adjusting the model according to the function.

Abstract: A machine learning method comprises: (a) applying a contrastive learning model to a training image and an image mask to generate a foreground feature vector pair and a background feature vector pair; (b) calculating a foreground loss and a background loss according to the foreground feature vector pair and the background feature vector pair; (c) calculating a total loss from the foreground loss and the background loss; (e) when a recursion end condition is met, using a first encoder for parameter adjustment of machine learning model; and (f) when the recursive end condition is not met, adjusting a parameter of the first encoder in the contrastive learning model using the total loss, and adjust a parameter of a second encoder in the contrastive learning model using the adjusted parameter of the first encoder and a preset multiple, thereby performing step (a) to step (d) again.

Abstract: An image augmentation device is provided, which includes a memory and a processor. The processor is configured for performing following operations: extracting a first object contour from a first image mask, wherein the first object contour corresponds to a first label; superimposing the first object contour to a superimposed region in a second image mask according to an augmentation parameter to generate a third image mask, wherein the augmentation parameter includes a contour scaling parameter, a contour moving distance, a contour rotation angle and a range which can be superimposed, and the third image mask includes the first object contour and a second object contour in the second image mask; and generating a sample image which corresponds to according to the first object contour and the second object contour in the third image mask by a generative adversarial network model for performing machine learning.