Adversarial Continual Learning (ECCV'2020)
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.
@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}
}- Step1: Set the path in
run_trainer.pywith./config/acl.yamlconfig = Config("./config/acl.yaml").get_config_dict()
- Step2: Run command
python run_trainer.py
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:
