Python Artificially increase my dataset size for Pix2pix Gan

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To enhance a small dataset of 250 image pairs for a Pix2pix GAN, several techniques can be employed to artificially increase its size. Data augmentation methods such as rotation, flipping, scaling, and color adjustments can create variations of existing images, effectively generating new training samples. Additionally, applying transformations like cropping or adding noise can further diversify the dataset. The concept of combining existing images to create new information is valid; slight modifications can help the neural network perceive these altered images as distinct, thus improving its learning capability. Overall, leveraging these augmentation strategies can significantly boost the performance of the GAN despite the limited initial dataset size.
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I am working on a Pix2pix Gan but I small dataset size of about 250 Pairs of images. What are good ways in code to artificially increase my dataset size?
 
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Do you think that it is possible to create new bits of information by combining existing bits?
 
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I read that if Slightly modified an image, it becomes like a new image to the neural network
 

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