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Semantic Augmentation in Images using Language

Created by
  • Haebom

Author

Sahiti Yerramilli, Jayant Sravan Tamarapalli, Tanmay Girish Kulkarni, Jonathan Francis, Eric Nyberg

Outline

This paper proposes a data augmentation technique using a diffusion model to solve the overfitting problem of deep learning models. Deep learning models require a large amount of labeled data, which causes overfitting and reduces the generalization ability to real environments. In this paper, we propose a method to augment existing datasets using a large dataset of a diffusion model that generates realistic images based on text inputs, and explore ways to improve the generalization performance of deep learning models across domains through various data augmentation strategies.

Takeaways, Limitations

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Takeaways:
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Data augmentation techniques using diffusion models can be effective in solving the overfitting problem of deep learning models and improving generalization performance to real environments.
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We present a novel approach that overcomes the size and quality constraints of existing datasets.
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Comparing and analyzing different data augmentation strategies can help you choose the optimal strategy.
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Limitations:
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The quality and realism of the images generated by a diffusion model can affect the model's performance.
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There is a possibility that the generated image may actually degrade performance due to the difference in distribution from the original dataset.
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The effectiveness of the proposed data augmentation technique may vary depending on the specific dataset and model.
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There may be a lack of detailed analysis of specific data augmentation strategies and their effectiveness.
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