Class incremental learning aims to solve a problem that arises when
continuously adding unseen class instances to an existing model This approach
has been extensively studied in the context of image classification; however
its applicability to object detection is not well established yet. Existing
frameworks using replay methods mainly collect replay data without considering
the model being trained and tend to rely on randomness or the number of labels
of each sample. Also, despite the effectiveness of the replay, it was not yet
optimized for the object detection task. In this paper, we introduce an
effective buffer training strategy (eBTS) that creates the optimized replay
buffer on object detection. Our approach incorporates guarantee minimum and
hierarchical sampling to establish the buffer customized to the trained model.
%These methods can facilitate effective retrieval of prior knowledge.
Furthermore, we use the circular experience replay training to optimally
utilize the accumulated buffer data. Experiments on the MS COCO dataset
demonstrate that our eBTS achieves state-of-the-art performance compared to the
existing replay schemes.

本文介绍了一种有效的缓冲训练策略 (eBTS)，用于在物体检测中创建经过优化的回放缓冲区，实验证明我们的 eBTS 达到了与现有的回放方案相比的最先进性能。

递增物体检测的分类缓冲区管理：一种有效的缓冲区训练策略

Class-Wise Buffer Management for Incremental Object Detection: An  Effective Buffer Training Strategy

Humans engage in learning and reviewing processes with curricula when
acquiring new skills or knowledge. This human learning behavior has inspired
the integration of curricula with replay methods in continual learning agents.
The goal is to emulate the human learning process, thereby improving knowledge
retention and facilitating learning transfer. Existing replay methods in
continual learning agents involve the random selection and ordering of data
from previous tasks, which has shown to be effective. However, limited research
has explored the integration of different curricula with replay methods to
enhance continual learning. Our study takes initial steps in examining the
impact of integrating curricula with replay methods on continual learning in
three specific aspects: the interleaved frequency of replayed exemplars with
training data, the sequence in which exemplars are replayed, and the strategy
for selecting exemplars into the replay buffer. These aspects of curricula
design align with cognitive psychology principles and leverage the benefits of
interleaved practice during replays, easy-to-hard rehearsal, and exemplar
selection strategy involving exemplars from a uniform distribution of
difficulties. Based on our results, these three curricula effectively mitigated
catastrophic forgetting and enhanced positive knowledge transfer, demonstrating
the potential of curricula in advancing continual learning methodologies.

本文旨在探讨不同课程与回放方法相结合对于持续学习的影响，研究表明课程设计的三个方面有效地缓解了灾难性遗忘并增强了积极的知识转移。

重播对于课程体系整合的影响：对于连续学习的影响

Integrating Curricula with Replays: Its Effects on Continual Learning

Continual learning, the setting where a learning agent is faced with a never
ending stream of data, continues to be a great challenge for modern machine
learning systems. In particular the online or "single-pass through the data"
setting has gained attention recently as a natural setting that is difficult to
tackle. Methods based on replay, either generative or from a stored memory,
have been shown to be effective approaches for continual learning, matching or
exceeding the state of the art in a number of standard benchmarks. These
approaches typically rely on randomly selecting samples from the replay memory
or from a generative model, which is suboptimal. In this work, we consider a
controlled sampling of memories for replay. We retrieve the samples which are
most interfered, i.e. whose prediction will be most negatively impacted by the
foreseen parameters update. We show a formulation for this sampling criterion
in both the generative replay and the experience replay setting, producing
consistent gains in performance and greatly reduced forgetting. We release an
implementation of our method at
this https URL

该论文探讨了在机器学习系统中，面对一个不断涌现的数据流的情况下，通过回放的方法来控制采样记忆，从而提供全新的方案来解决单通道数据训练面临的挑战，并通过多个标准基准测试，达到了与或超过现有最先进技术的表现。