Artificially increase my dataset size for Pix2pix Gan

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SUMMARY

The discussion focuses on strategies to artificially increase dataset size for Pix2pix GANs when starting with a limited dataset of 250 image pairs. Techniques such as image augmentation, including slight modifications like rotation, flipping, and color adjustments, are confirmed to create new training examples that can enhance model performance. The consensus is that these modifications effectively provide the neural network with diverse inputs, improving its ability to generalize from the limited dataset.

PREREQUISITES
  • Understanding of Pix2pix GAN architecture
  • Familiarity with image augmentation techniques
  • Basic knowledge of Python programming
  • Experience with deep learning frameworks such as TensorFlow or PyTorch
NEXT STEPS
  • Research image augmentation libraries like Keras ImageDataGenerator
  • Explore advanced techniques such as style transfer for dataset expansion
  • Learn about data synthesis methods in GANs
  • Investigate the impact of dataset size on GAN performance
USEFUL FOR

Machine learning practitioners, data scientists, and researchers working with generative adversarial networks, particularly those looking to enhance training datasets for image generation tasks.

btb4198
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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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