Adversarial Continual Learning (ECCV'2020)

Abstract

Continual learning aims to learn new tasks without forgetting previously learned ones. We hypothesize that representations learned to solve each task in a sequence have a shared structure while containing some task-specific properties. We show that shared features are significantly less prone to forgetting and propose a novel hybrid continual learning framework that learns a disjoint representation for task-invariant and task-specific features required to solve a sequence of tasks. Our model combines architecture growth to prevent forgetting of task-specific skills and an experience replay approach to preserve shared skills. We demonstrate our hybrid approach is effective in avoiding forgetting and show it is superior to both architecture-based and memory-based approaches on class incrementally learning of a single dataset as well as a sequence of multiple datasets in image classification. Our code is available at https://github.com/facebookresearch/Adversarial-Continual-Learning.

Citation

@article{ebrahimi2020adversarial,
  title={Adversarial Continual Learning},
  author={Ebrahimi, Sayna and Meier, Franziska and Calandra, Roberto and Darrell, Trevor and Rohrbach, Marcus},
  journal={arXiv preprint arXiv:2003.09553},
  year={2020}
}

How to Reproduce ACL

• Step1: Set the path in run_trainer.py with ./config/acl.yaml

  config = Config("./config/acl.yaml").get_config_dict()

• Step2: Run command

  python run_trainer.py

Results on CIFAR-100 dataset

The training process is documented in the file log.txt

Arch	Input Size	Batch Size	Buffer Size	Epochs	Task Number	Average ACC
AlexNet-ACL	3x32x32	64	0	30	20	77.73

The accuracies for 20 tasks are shown below: