Real-world applications require the classification model to adapt to new
classes without forgetting old ones. Correspondingly, Class-Incremental
Learning (CIL) aims to train a model with limited memory size to meet this
requirement. Typical CIL methods tend to save representative exemplars from
former classes to resist forgetting, while recent works find that storing
models from history can substantially boost the performance. However, the
stored models are not counted into the memory budget, which implicitly results
in unfair comparisons. We find that when counting the model size into the total
budget and comparing methods with aligned memory size, saving models do not
consistently work, especially for the case with limited memory budgets. As a
result, we need to holistically evaluate different CIL methods at different
memory scales and simultaneously consider accuracy and memory size for
measurement. On the other hand, we dive deeply into the construction of the
memory buffer for memory efficiency. By analyzing the effect of different
layers in the network, we find that shallow and deep layers have different
characteristics in CIL. Motivated by this, we propose a simple yet effective
baseline, denoted as MEMO for Memory-efficient Expandable MOdel. MEMO extends
specialized layers based on the shared generalized representations, efficiently
extracting diverse representations with modest cost and maintaining
representative exemplars. Extensive experiments on benchmark datasets validate
MEMO's competitive performance. Code is available at:
this https URL

本文主要讨论了在有限内存情况下如何处理历史数据和新类别的增加，并提出了一种名为 MEMO 的基于共享泛化表示的基准，在保持代表性示例的同时，具有高效的内存管理和提取多样性特征的能力。

603 样本模型：走向内存高效的类增量学习

A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental  Learning

3D object recognition accuracy can be improved by learning the multi-scale
spatial features from 3D spatial geometric representations of objects such as
point clouds, 3D models, surfaces, and RGB-D data. Current deep learning
approaches learn such features either using structured data representations
(voxel grids and octrees) or from unstructured representations (graphs and
point clouds). Learning features from such structured representations is
limited by the restriction on resolution and tree depth while unstructured
representations creates a challenge due to non-uniformity among data samples.
In this paper, we propose an end-to-end multi-level learning approach on a
multi-level voxel grid to overcome these drawbacks. To demonstrate the utility
of the proposed multi-level learning, we use a multi-level voxel representation
of 3D objects to perform object recognition. The multi-level voxel
representation consists of a coarse voxel grid that contains volumetric
information of the 3D object. In addition, each voxel in the coarse grid that
contains a portion of the object boundary is subdivided into multiple
fine-level voxel grids. The performance of our multi-level learning algorithm
for object recognition is comparable to dense voxel representations while using
significantly lower memory.

本文提出了一种基于多层体素网格的端到端多级学习方法来解决现有 3D 目标识别中结构化和非结构化数据表示的缺陷，结果表明该方法在节约内存的同时能够实现与密集体素表示相当的目标识别性能。