This work proposes a minimal computational model for learning structured memories of multiple object classes in an incremental setting. Our approach is based on establishing a closed-loop transcription between the classes and a corresponding set of subspaces, known as a linear discriminative representation, in a low-dimensional feature space. Our method is simpler than existing approaches for incremental learning, and more efficient in terms of model size, storage, and computation: it requires only a single, fixed-capacity autoencoding network with a feature space that is used for both discriminative and generative purposes. Network parameters are optimized simultaneously without architectural manipulations, by solving a constrained minimax game between the encoding and decoding maps over a single rate reduction-based objective. Experimental results show that our method can effectively alleviate catastrophic forgetting, achieving significantly better performance than prior work of generative replay on MNIST, CIFAR-10, and ImageNet-50, despite requiring fewer resources. Source code can be found at https://github.com/tsb0601/i-CTRL

翻译：本文提出了一种用于在增量学习场景中学习多类结构化记忆的极简计算模型。该方法通过建立类别与低维特征空间中一组对应子空间（即线性判别表示）之间的闭环转录机制实现。相较于现有增量学习方法，本方法更为简洁，且在模型规模、存储和计算方面具有更高效率：仅需单一固定容量的自编码网络，其特征空间同时服务于判别与生成任务。通过求解编码与解码映射之间基于单一速率缩减目标的约束极小极大博弈，网络参数可在不进行架构调整的情况下同步优化。实验结果表明，该方法能有效缓解灾难性遗忘：尽管所需资源更少，其在MNIST、CIFAR-10和ImageNet-50数据集上仍取得了显著优于生成回放类方法的性能。源代码见https://github.com/tsb0601/i-CTRL