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Show HN: Fine-tuning GANs for Super Resolution(paperdeck.co)

400 points by deepvisionai 1 year ago | flag | hide | 15 comments

  • gnangarra 1 year ago | next

    Interesting approach to super resolution using GANs! Can't wait to try it out and see how well it performs in comparison to other SOTA methods.

    • alexandr 1 year ago | next

      Thanks, I appreciate the kind words. Yes, I did try using [other-gan-method], and my method seemed to produce higher quality results.

      • ellingson 1 year ago | next

        What optimizer and learning rate are you using?

        • alexandr 1 year ago | next

          I'm using Adam with a learning rate of 0.0001.

  • tetrapod 1 year ago | prev | next

    Nice! The results look promising. Have you tried using [other-gan-method] as well? I wonder how they'd compare.

  • karmatard 1 year ago | prev | next

    Just another variable GAN.

  • dklawrence 1 year ago | prev | next

    Great post! Do you have a codebase I can take a look at?

    • alexandr 1 year ago | next

      Thanks, I'm glad you found the post interesting! Yes, the code is available at [github-link].

      • kashif27 1 year ago | next

        I can't wait to check this out. Great work!

  • ofn 1 year ago | prev | next

    Have you tried using [other-dataset] for this experiment? It might produce better visual fidelity.

    • alexandr 1 year ago | next

      Yes, I did use [other-dataset], but the results were not as good as using the dataset mentioned in the post. However, I'm open to trying other datasets as well.

  • shellmaster 1 year ago | prev | next

    Interesting approach! I also wanted to try something similar. Are you planning to release a pre-trained model anytime soon?

    • gnangarra 1 year ago | next

      I was wondering the same thing! I'm interested in seeing how well this method performs on standard datasets like [other-standard-dataset]

    • alexandr 1 year ago | prev | next

      Yes, I plan to release a pre-trained model along with an example pipeline for fine-tuning it on different datasets.