In this work we introduce an approach for incremental learning, which preserves feature descriptors instead of images unlike most existing work. Keeping such low-dimensional embeddings instead of images reduces the memory footprint significantly. We assume that the model is updated incrementally for new classes as new data becomes available sequentially. This requires adapting the previously stored feature vectors to the updated feature space without having access to the corresponding images. Feature adaptation is learned with a multi-layer perceptron, which is trained on feature pairs of an image corresponding to the outputs of the original and updated network. We validate experimentally that such a transformation generalizes well to the features of the previous set of classes, and maps features to a discriminative subspace in the feature space. As a result, the classifier is optimized jointly over new and old classes without requiring old class images. Experimental results show that our method achieves state-of-the-art classification accuracy in incremental learning benchmarks, while having at least an order of magnitude lower memory footprint compared to image preserving strategies.

本文提出了一种增量学习的方法，该方法保留以前学习类别的训练图像的特征描述符，而不是图像本身，采用了比大多数现有方法更少的低维特征嵌入，通过多层感知器学习特征适应以实现分类器旧类别和新类别的联合优化，并取得了与保留图像相比更低的内存占用和最先进的分类准确率。

通过特征适应实现内存高效的渐进式学习