通过闭环转录进行结构化记忆的增量学习
Incremental Learning of Structured Memory via Closed-Loop Transcription
AI总结：
本文提出 i-CTRL，通过单一固定容量自编码网络和率缩减极小极大目标建立类别与子空间的闭环转录，以更少资源缓解增量学习中的灾难性遗忘。
摘要：
本工作提出了一个极简计算模型，用于在增量设置中学习多个对象类别的结构化记忆。我们的方法基于在类别与低维特征空间中对应的一组子空间（称为线性判别表示）之间建立闭环转录。我们的方法比现有增量学习方法更简单，并且在模型规模、存储和计算方面更高效：它只需要一个容量固定的自编码网络，其特征空间同时用于判别和生成目的。通过在单个基于率缩减的目标上求解编码映射与解码映射之间的约束极小极大博弈，网络参数被同时优化，而无需架构改动。实验结果表明，我们的方法能够有效缓解灾难性遗忘，尽管所需资源更少，但在 MNIST、CIFAR-10 和 ImageNet-50 上取得了显著优于先前生成重放工作的性能。源代码可在 https://github.com/tsb0601/i-CTRL 找到。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
