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Deep Learning project

Missions

You must find a way to restore/decrypt images!

Two challenges are given.

  1. Image restoration
  • Dataset 1A: images only need to be restored, examples of damaged and restored version of images are provided.
  • Dataset 1B: images have to be restored using advanced techniques, examples of damaged and restored version of images are also provided.
  1. Image decryption
  • Dataset 2: images are clearly encrypted... but some of them have been cracked!!! Examples of encrypted images and corresponding original images are provided.

Both train and test sets are provided.

Rules

  • You must use Deep Learning techniques.
  • Results must be reproductible, use torch.manual_seed(1234). Training must also be reproductible.
  • Team size <= 4
  • The following evaluation metric will be used.
def eval_metric(img, pred):
     return torch.abs(img - pred).sum()
  • Do not use the test set during training... The test set cannot be used to train or select you model. test set + eval_metric can be used to compare your results with other teams.
  • For each challenge, a bonus will be given to the best team (the one maximizing eval_metric on the test set). Best team bonus will be +2/#number of team members (challenge 1, average between Dataset 1A, and 1B will be made).

You should submit:

  • Trained models.
  • A report detailing the methodology, tested architectures, results, illustration of predictions on the test set, as well as discussions. You must report the eval_metric results for the test sets (max 20 pages).