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Cost-Free-Incremental-Learning

This is the code for Cost-Free-Incremental-Learning which is accepted on ICLR 2022 Spotlight paper.

Looking Back on Learned Experiences For Class/task Incremental Learning

Abstract

Existing methods in IL tackle catastrophic forgetting by either storing old data samples or only updating a parameter set of deep neural networks, which, however, demands a large memory budget or spoils the flexibility of models to learn the incremented task distribution. In this paper, we shed light on an on-call transfer set to provide past experiences whenever a new task arises in the data stream. In particular, we propose a CostFree Incremental Learning (CF-IL) not only to replay past experiences the model has learned but also to perform this in a cost free manner. Towards this end, we introduced a memory recovery paradigm in which we query the network to synthesize past exemplars whenever a new task emerges. Thus, our method needs no extra memory for data buffering or network growing, besides calls the proposed memory recovery paradigm to provide past exemplars, named a transfer set in order to mitigate catastrophically forgetting the former tasks in the Incremental Learning (IL) setup. Moreover, in contrast with recently proposed methods, the suggested paradigm does not desire a parallel architecture since it only relies on the learner network. Compared to the state-of-the-art data techniques without buffering past data samples, CF-IL demonstrates significantly better performance on the well-known datasets whether a task oracle is available in test time (Task-IL) or not (Class-IL).

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Memory Recovery Paradigm (MRP)

To better understanding the MRP, we provide This Notebook to visualize the process of recovering data from an arbitrary learner network. For the simplisity, we use one-hot vector instead of modeling the network output space using Dirichlet distribution. You can set target_class to an arbitrary class id to see the recovered sample.

To generate recovered samples : python run_MRP.py --dataset=[mnist, cifar10] --t_train=False --num_sample=4000 --batch_size=200

Recovered data for CIFAR-10 dataset and pretrained models are available here.

Citing

@inproceedings{pourkeshavarzi2021looking,
  title={Looking Back on Learned Experiences For Class/task Incremental Learning},
  author={PourKeshavarzi, Mozhgan and Zhao, Guoying and Sabokrou, Mohammad},
  booktitle={International Conference on Learning Representations},
  year={2021}
}

Reference

Our implementation references the codes in the following repositories: