We introduce a rehearsal-free federated domain incremental learning
framework, RefFiL, based on a global prompt-sharing paradigm to alleviate
catastrophic forgetting challenges in federated domain-incremental learning,
where unseen domains are continually learned. Typical methods for mitigating
forgetting, such as the use of additional datasets and the retention of private
data from earlier tasks, are not viable in federated learning (FL) due to
devices' limited resources. Our method, RefFiL, addresses this by learning
domain-invariant knowledge and incorporating various domain-specific prompts
from the domains represented by different FL participants. A key feature of
RefFiL is the generation of local fine-grained prompts by our domain adaptive
prompt generator, which effectively learns from local domain knowledge while
maintaining distinctive boundaries on a global scale. We also introduce a
domain-specific prompt contrastive learning loss that differentiates between
locally generated prompts and those from other domains, enhancing RefFiL's
precision and effectiveness. Compared to existing methods, RefFiL significantly
alleviates catastrophic forgetting without requiring extra memory space, making
it ideal for privacy-sensitive and resource-constrained devices.

我们引入了一种无需复习的联邦域增量学习框架 RefFiL，基于全局提示共享范式，以缓解联邦域增量学习中的灾难性遗忘挑战，不需要额外的内存空间，适用于隐私敏感和资源受限设备。

无需演练的联邦领域增量学习

Rehearsal-free Federated Domain-incremental Learning

We consider the problem of learning multiple tasks in a continual learning
setting in which data from different tasks is presented to the learner in a
streaming fashion. A key challenge in this setting is the so-called
"catastrophic forgetting problem", in which the performance of the learner in
an "old task" decreases when subsequently trained on a "new task". Existing
continual learning methods, such as Averaged Gradient Episodic Memory (A-GEM)
and Orthogonal Gradient Descent (OGD), address catastrophic forgetting by
minimizing the loss for the current task without increasing the loss for
previous tasks. However, these methods assume the learner knows when the task
changes, which is unrealistic in practice. In this paper, we alleviate the need
to provide the algorithm with information about task changes by using an online
clustering-based approach on a dynamically updated finite pool of samples or
gradients. We thereby successfully counteract catastrophic forgetting in one of
the hardest settings, namely: domain-incremental learning, a setting for which
the problem was previously unsolved. We showcase the benefits of our approach
by applying these ideas to projection-based methods, such as A-GEM and OGD,
which lead to task-agnostic versions of them. Experiments on real datasets
demonstrate the effectiveness of the proposed strategy and its promising
performance compared to state-of-the-art methods.

通过使用基于在线聚类的方法解决连续学习中的灾难性遗忘问题，成功地应用于域增量学习，并展示了这种方法在真实数据集上的有效性及与最先进方法的优越性能。

基于聚类的领域增量学习

Clustering-based Domain-Incremental Learning

Machine Learning (ML) models struggle with data that changes over time or
across domains due to factors such as noise, occlusion, illumination, or
frequency, unlike humans who can learn from such non independent and
identically distributed data. Consequently, a Continual Learning (CL) approach
is indispensable, particularly, Domain-Incremental Learning. In this paper, we
propose a novel pipeline for identifying tasks in domain-incremental learning
scenarios without supervision. The pipeline comprises four steps. First, we
obtain base embeddings from the raw data using an existing transformer-based
model. Second, we group the embedding densities based on their similarity to
obtain the nearest points to each cluster centroid. Third, we train an
incremental task classifier using only these few points. Finally, we leverage
the lightweight computational requirements of the pipeline to devise an
algorithm that decides in an online fashion when to learn a new task using the
task classifier and a drift detector. We conduct experiments using the SODA10M
real-world driving dataset and several CL strategies. We demonstrate that the
performance of these CL strategies with our pipeline can match the ground-truth
approach, both in classical experiments assuming task boundaries, and also in
more realistic task-agnostic scenarios that require detecting new tasks
on-the-fly

本研究提出了一种新的无监督域增量学习识别任务的流程，该流程包括从原始数据中获取基础嵌入，根据它们与每个聚类中心的相似度获取最近的点，使用这些点训练增量任务分类器，并通过任务分类器和漂移检测器设计了一种算法来决定何时在线学习新任务。

TADIL：通过使用 Transformer 最近中心嵌入进行任务 ID 推断的任务无关域增量学习

TADIL: Task-Agnostic Domain-Incremental Learning through Task-ID  Inference using Transformer Nearest-Centroid Embeddings

In autonomous driving scenarios, current object detection models show strong
performance when tested in clear weather. However, their performance
deteriorates significantly when tested in degrading weather conditions. In
addition, even when adapted to perform robustly in a sequence of different
weather conditions, they are often unable to perform well in all of them and
suffer from catastrophic forgetting. To efficiently mitigate forgetting, we
propose Domain-Incremental Learning through Activation Matching (DILAM), which
employs unsupervised feature alignment to adapt only the affine parameters of a
clear weather pre-trained network to different weather conditions. We propose
to store these affine parameters as a memory bank for each weather condition
and plug-in their weather-specific parameters during driving (i.e. test time)
when the respective weather conditions are encountered. Our memory bank is
extremely lightweight, since affine parameters account for less than 2% of a
typical object detector. Furthermore, contrary to previous domain-incremental
learning approaches, we do not require the weather label when testing and
propose to automatically infer the weather condition by a majority voting
linear classifier.

提出了一种基于领域增量学习的解决方案 (Domain-Incremental Learning through Activation Matching, DILAM)，通过无监督特征对齐，将预训练模型适应于不同的天气条件，存储不同天气条件下的仿射参数，并在测试时利用轻量级的存储来自动推断天气状况，从而有效地解决了自动驾驶情景下目标检测模型在多变的天气条件下表现不佳以及遗忘的问题。