Few-shot class-incremental learning (FSCIL) aims to design machine learning
algorithms that can continually learn new concepts from a few data points,
without forgetting knowledge of old classes. The difficulty lies in that
limited data from new classes not only lead to significant overfitting issues
but also exacerbate the notorious catastrophic forgetting problems. Moreover,
as training data come in sequence in FSCIL, the learned classifier can only
provide discriminative information in individual sessions, while FSCIL requires
all classes to be involved for evaluation. In this paper, we address the FSCIL
problem from two aspects. First, we adopt a simple but effective decoupled
learning strategy of representations and classifiers that only the classifiers
are updated in each incremental session, which avoids knowledge forgetting in
the representations. By doing so, we demonstrate that a pre-trained backbone
plus a non-parametric class mean classifier can beat state-of-the-art methods.
Second, to make the classifiers learned on individual sessions applicable to
all classes, we propose a Continually Evolved Classifier (CEC) that employs a
graph model to propagate context information between classifiers for
adaptation. To enable the learning of CEC, we design a pseudo incremental
learning paradigm that episodically constructs a pseudo incremental learning
task to optimize the graph parameters by sampling data from the base dataset.
Experiments on three popular benchmark datasets, including CIFAR100,
miniImageNet, and Caltech-USCD Birds-200-2011 (CUB200), show that our method
significantly outperforms the baselines and sets new state-of-the-art results
with remarkable advantages.

本文提出了一种基于分离学习策略的持续演化分类器（CEC）框架，该框架在每个增量会话中只更新分类器，从而避免了表示的知识遗忘问题，并采用伪增量学习方法优化图参数来构建持续的上下文信息传递网络，以解决少样本类别增量学习（FSCIL）的课程先决问题，实验结果表明该方法在 CIFAR100、miniImageNet 和 Caltech-USCD Birds-200-2011（CUB200）等基准数据集上表现出 remarkable 的优势。

持续进化的分类器用于小样本增量学习

Few-Shot Incremental Learning with Continually Evolved Classifiers

Current reinforcement learning (RL) methods can successfully learn single
tasks but often generalize poorly to modest perturbations in task domain or
training procedure. In this work, we present a decoupled learning strategy for
RL that creates a shared representation space where knowledge can be robustly
transferred. We separate learning the task representation, the forward
dynamics, the inverse dynamics and the reward function of the domain, and show
that this decoupling improves performance within the task, transfers well to
changes in dynamics and reward, and can be effectively used for online
planning. Empirical results show good performance in both continuous and
discrete RL domains.

本文提出了一种分离式学习策略，通过创造共享的表示空间来保证知识可以稳健地转移，分离学习任务表示，前向动力学，反向动力学和领域奖励函数，并表明这种分离可以提高任务内的性能并有效用于在线计划，在连续和离散 RL 领域中表现良好。