Exemplar-Free Class Incremental Learning (EFCIL) tackles the problem of training a model on a sequence of tasks without access to past data. Existing state-of-the-art methods represent classes as Gaussian distributions in the feature extractor's latent space, enabling Bayes classification or training the classifier by replaying pseudo features. However, we identify two critical issues that compromise their efficacy when the feature extractor is updated on incremental tasks. First, they do not consider that classes' covariance matrices change and must be adapted after each task. Second, they are susceptible to a task-recency bias caused by dimensionality collapse occurring during training. In this work, we propose AdaGauss -- a novel method that adapts covariance matrices from task to task and mitigates the task-recency bias owing to the additional anti-collapse loss function. AdaGauss yields state-of-the-art results on popular EFCIL benchmarks and datasets when training from scratch or starting from a pre-trained backbone. The code is available at: https://github.com/grypesc/AdaGauss.

本研究解决了在无示例类别增量学习（EFCIL）中模型在序列任务训练时面临的两大关键问题：协方差矩阵未适应的变化和任务近期偏差。我们提出的AdaGauss方法通过任务间调整协方差矩阵和引入抗崩溃损失函数，有效缓解了这些问题，并在EFCIL基准测试中取得了最先进的结果。

任务近期偏差反击：在无示例的类别增量学习中调整协方差