A technique that uses deep neural networks to apply the artistic style of one image (the style image) to the content of another (the content image). It typically involves optimizing a new image to minimize the difference in content with the content image and the difference in style with the style image, using features extracted from a pre-trained convolutional neural network (usually VGG). Common use cases include generating artwork or augmenting datasets with stylistic variations.
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