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To avoid these problems, this paper proposes a method, "Retrospective Adversarial

This section contains the theoretical analysis of the loss functions of offline experience replay (Proposition 2),augmented experience replay (Proposition 3),andonline experience replay with reservoirsampling(Proposition1). For all experiments, we use the learning rate of 0.1 following the same setting as in Aljundi et al. [2019], Shimetal.[2021], This paper uses Randaugment [Cubuk et al., 2020], which is an auto augmentation method. It randomly selectsP augmentation operators from a set of 14 operators and applies them to the images. ToapplyBPGintheOCLenvironment,weproposeto determine the better/worse action set based on the feedback in the form of current memory batch accuracyAM,which reflects the memory overfitting level of the CL agent.

That is,we optimize for theα and β hyperparameters while fixing theσ to a negligible amount (σ = 2 32 specifically).

Continual learning is an emerging research challenge in machine learning that addresses the problem where models quickly fit the most recently trained-on data but suffer from catastrophic forgetting of previous data due to distribution shifts --- it does this by maintaining a small historical replay buffer in replay-based methods. To avoid these problems, this paper proposes a method, ``Retrospective Adversarial Replay (RAR)'', that synthesizes adversarial samples near the forgetting boundary. RAR perturbs a buffered sample towards its nearest neighbor drawn from the current task in a latent representation space. By replaying such samples, we are able to refine the boundary between previous and current tasks, hence combating forgetting and reducing bias towards the current task. To mitigate the severity of a small replay buffer, we develop a novel MixUp-based strategy to increase replay variation by replaying mixed augmentations. Combined with RAR, this achieves a holistic framework that helps to alleviate catastrophic forgetting. We show that this excels on broadly-used benchmarks and outperforms other continual learning baselines especially when only a small buffer is available. We conduct a thorough ablation study over each key component as well as a hyperparameter sensitivity analysis to demonstrate the effectiveness and robustness of RAR.

Data parallelism can boost the training speed of convolutional neural networks (CNN), but could suffer from significant communication costs caused by gradient aggregation.

Here, if we use the model's last layer denoted by

To avoid these problems, this paper proposes a method, "Retrospective Adversarial

Note 3: Consider a balanced continual learning dataset (e.g., Split-CIFAR100, Split-Mini-ImageNet) Note 4: Consider general continual learning datasets. The hyperparameter settings are summarized in Table 4. All models are optimized using vanilla SGD. For all experiments, we use the learning rate of 0.1 following the same setting as in Aljundi et al. Mai et al. reported (2021) considerable and consistent performance gains when replacing the Softmax classifier with the NCM classifier.