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emnist-dataset

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Neural-Compression-with-Autoencoders

Exploring advanced autoencoder architectures for efficient data compression on EMNIST dataset, focusing on high-fidelity image reconstruction with minimal information loss. This project tests various encoder-decoder configurations to optimize performance metrics like MSE, SSIM, and PSNR, aiming to achieve near-lossless data compression.

  • Updated Jun 1, 2024
  • Jupyter Notebook

SCRNet is a deep neural network architecture designed to handle compressed and noisy character images signals. Tailored for tasks like character recognition and image signal restoration, SCRNet integrates classification and reconstruction pathways, enhancing performance and robustness through their synergistic interaction

  • Updated Sep 26, 2024
  • Jupyter Notebook

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