Code for CL3: Generalization of Contrastive Loss for Lifelong Learning.

Lifelong learning portrays learning gradually in nonstationary environments and emulates the process of human learning, which is efficient, robust, and able to learn new concepts incrementally from sequential experience. To equip neural networks with such a capability, one needs to overcome the problem of catastrophic forgetting, the phenomenon of forgetting past knowledge while learning new concepts. In this work, we propose a novel knowledge distillation algorithm that makes use of contrastive learning to help a neural network to preserve its past knowledge while learning from a series of tasks. Our proposed generalized form of contrastive distillation strategy tackles catastrophic forgetting of old knowledge, and minimizes semantic drift by maintaining a similar embedding space, as well as ensures compactness in feature distribution to accommodate novel tasks in a current model. Our comprehensive study shows that our method achieves improved performances in the challenging class-incremental, task-incremental, and domain-incremental learning for supervised scenarios.

This repository contains the implementation of the paper 'CL3: Generalization of Contrastive Loss for Lifelong Learning'.

If you find this code useful, please reference in our paper:

@article{roy2023cl3,
  title={CL3: Generalization of Contrastive Loss for Lifelong Learning},
  author={Roy, Kaushik and Simon, Christian and Moghadam, Peyman and Harandi, Mehrtash},
  journal={Journal of Imaging},
  volume={9},
  number={12},
  pages={259},
  year={2023},
  publisher={MDPI}
}

Unlike other methods for lifelong learning, CL3 demands a preliminary training phase for discrimiative feature learning and adaptation of feature space to adapt novel knowledge, driven by its adoption of a contrastive representation learning-based distillation strategy. Therefore, the results presented in our paper can be obtained via linear evaluation using representations extracted from pre-trained backbone. Please follow the instructions outlined below.

python main.py --batch_size 512 --model resnet18 --dataset cifar10 --mem_size 200 --epochs 100 --start_epoch 500 --learning_rate 0.5 --temp 0.5  --cosine --syncBN

python main_linear_buffer.py --learning_rate 1 --target_task 4 --ckpt ./save_random_200/cifar10_models/cifar10_32_resnet18_lr_0.5_decay_0.0001_bsz_512_temp_0.5_momentum_1.000_trial_0_500_100_1.0_cosine_warm/ --logpt ./save_random_200/logs/cifar10_32_resnet18_lr_0.5_decay_0.0001_bsz_512_temp_0.5_momentum_1.000_trial_0_500_100_1.0_cosine_warm/

If you have troulbe with NaN loss while training representation learning, you may find solutions from SupCon issue page. Please check your training works perfectly on SupCon first.

We would like to acknowledge the authors of Co^2L: Contrastive Continual Learning for their excellent codebase which has been used as a starting point for this project.