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DeepModelConsolidation

  • It is the same rehearsal method as iCaRL. Incremental Learning reuses existing data to update new data, so it tends to create models that are biased against old or new classes.
  • This results in information asymmetric, which results in performance degradation due to Catastopic Forgetting. To solve this problem, this paper proposes a new class incremental learning paradigm called Deep Model Consolidation (DMC) Method is described in more detail in the original paper.

Usage

For running the code you should follow these steps:

  1. Download CIFAR-100 and ImageNet32 test set in the proper folders in the provided folder structure.
  2. Run data_prep.ipynb
  3. Choose the configuration you wish to run in the configuration_setup.txt and copy the provided parameters in the proper code cell in DMC.ipynb
  4. Run DMC.ipynb
  5. Upon completion run Graphing_solutions.ipynb

Note 1. If you wish to test some other depth of residual network the code is provided to do that on the entire CIFAR-100 dataset. Just change the resnet_type parameter and run Resnet32.ipynb

Note 2. Every jupyter notebook provided is meant to be run on Google Colab platform.

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