This paper focuses on the prevalent performance imbalance in the stages of incremental learning. To avoid obvious stage learning bottlenecks, we propose a brand-new stage-isolation based incremental learning framework, which leverages a series of stage-isolated classifiers to perform the learning task of each stage without the interference of others. To be concrete, to aggregate multiple stage classifiers as a uniform one impartially, we first introduce a temperature-controlled energy metric for indicating the confidence score levels of the stage classifiers. We then propose an anchor-based energy self-normalization strategy to ensure the stage classifiers work at the same energy level. Finally, we design a voting-based inference augmentation strategy for robust inference. The proposed method is rehearsal free and can work for almost all continual learning scenarios. We evaluate the proposed method on four large benchmarks. Extensive results demonstrate the superiority of the proposed method in setting up new state-of-the-art overall performance. \emph{Code is available at} \url{https://github.com/iamwangyabin/ESN}.

持续学习背后的主流范例一直在使模型参数调整到非静止数据分布，灾难性遗忘是中央挑战。典型方法在测试时间依赖排练缓冲区或已知的任务标识，以检索学到的知识和地址遗忘，而这项工作呈现了一个新的范例，用于持续学习，旨在训练更加简洁的内存系统而不在测试时间访问任务标识。我们的方法学会动态提示（L2P）预先训练的模型，以在不同的任务转换下顺序地学习任务。在我们提出的框架中，提示是小型可学习参数，这些参数在内存空间中保持。目标是优化提示，以指示模型预测并明确地管理任务不变和任务特定知识，同时保持模型可塑性。我们在流行的图像分类基准下进行全面的实验，具有不同挑战的持续学习环境，其中L2P始终如一地优于现有最先进的方法。令人惊讶的是，即使没有排练缓冲区，L2P即使没有排练缓冲，L2P也能实现竞争力的结果，并直接适用于具有挑战性的任务不可行的持续学习。源代码在https://github.com/google-Research/l2p中获得。

最新的深层神经网络仍在努力解决持续学习中的灾难性遗忘问题。在本文中，我们提出了一种简单的范式（称为S宣传）和两种具体方法，以高度降低最典型的连续学习场景之一，即域增量学习（DIL）。范式的关键思想是通过预先训练的变压器独立学习提示，以避免使用常规方法中通常出现的示例。这导致了双赢游戏，提示可以为每个域获得最佳状态。跨域的独立提示仅请求一个单一的跨凝结损失，以进行训练，而一个简单的K-NN操作作为推理的域标识符。学习范式得出了图像及时的学习方法和全新的语言图像及时学习方法。拥有出色的可伸缩性（每个域的参数增加0.03％），我们最好的方法在三个标准的最先进的无典范方法上实现了显着的相对改进（平均约30％）当他们使用示例时，DIL任务甚至相对超过了他们的最好的任务。

持续学习旨在使单个模型能够学习一系列任务，而不会造成灾难性的遗忘。表现最好的方法通常需要排练缓冲区来存储过去的原始示例以进行经验重播，但是，由于隐私和内存约束，这会限制其实际价值。在这项工作中，我们提出了一个简单而有效的框架，即DualPrompt，该框架学习了一组称为提示的参数，以正确指示预先训练的模型，以依次学习到达的任务，而不会缓冲过去的示例。 DualPrompt提出了一种新颖的方法，可以将互补提示附加到预训练的主链上，然后将目标提出为学习任务不变和特定于任务的“指令”。通过广泛的实验验证，双启示始终在具有挑战性的课堂开发环境下始终设置最先进的表现。尤其是，双启示的表现优于最近的高级持续学习方法，其缓冲尺寸相对较大。我们还引入了一个更具挑战性的基准Split Imagenet-R，以帮助概括无连续的持续学习研究。源代码可在https://github.com/google-research/l2p上找到。

人类智慧的主食是以不断的方式获取知识的能力。在Stark对比度下，深网络忘记灾难性，而且为此原因，类增量连续学习促进方法的子字段逐步学习一系列任务，将顺序获得的知识混合成综合预测。这项工作旨在评估和克服我们以前提案黑暗体验重播（Der）的陷阱，这是一种简单有效的方法，将排练和知识蒸馏结合在一起。灵感来自于我们的思想不断重写过去的回忆和对未来的期望，我们赋予了我的能力，即我的能力来修改其重播记忆，以欢迎有关过去数据II的新信息II）为学习尚未公开的课程铺平了道路。我们表明，这些策略的应用导致了显着的改进;实际上，得到的方法 - 被称为扩展-DAR（X-DER） - 优于标准基准（如CiFar-100和MiniimAgeNet）的技术状态，并且这里引入了一个新颖的。为了更好地了解，我们进一步提供了广泛的消融研究，以证实并扩展了我们以前研究的结果（例如，在持续学习设置中知识蒸馏和漂流最小值的价值）。

Despite significant advances, the performance of state-of-the-art continual learning approaches hinges on the unrealistic scenario of fully labeled data. In this paper, we tackle this challenge and propose an approach for continual semi-supervised learning -- a setting where not all the data samples are labeled. An underlying issue in this scenario is the model forgetting representations of unlabeled data and overfitting the labeled ones. We leverage the power of nearest-neighbor classifiers to non-linearly partition the feature space and learn a strong representation for the current task, as well as distill relevant information from previous tasks. We perform a thorough experimental evaluation and show that our method outperforms all the existing approaches by large margins, setting a strong state of the art on the continual semi-supervised learning paradigm. For example, on CIFAR100 we surpass several others even when using at least 30 times less supervision (0.8% vs. 25% of annotations).

We motivate Energy-Based Models (EBMs) as a promising model class for continual learning problems. Instead of tackling continual learning via the use of external memory, growing models, or regularization, EBMs change the underlying training objective to cause less interference with previously learned information. Our proposed version of EBMs for continual learning is simple, efficient, and outperforms baseline methods by a large margin on several benchmarks. Moreover, our proposed contrastive divergence-based training objective can be combined with other continual learning methods, resulting in substantial boosts in their performance. We further show that EBMs are adaptable to a more general continual learning setting where the data distribution changes without the notion of explicitly delineated tasks. These observations point towards EBMs as a useful building block for future continual learning methods.

Lifelong person re-identification (LReID) is in significant demand for real-world development as a large amount of ReID data is captured from diverse locations over time and cannot be accessed at once inherently. However, a key challenge for LReID is how to incrementally preserve old knowledge and gradually add new capabilities to the system. Unlike most existing LReID methods, which mainly focus on dealing with catastrophic forgetting, our focus is on a more challenging problem, which is, not only trying to reduce the forgetting on old tasks but also aiming to improve the model performance on both new and old tasks during the lifelong learning process. Inspired by the biological process of human cognition where the somatosensory neocortex and the hippocampus work together in memory consolidation, we formulated a model called Knowledge Refreshing and Consolidation (KRC) that achieves both positive forward and backward transfer. More specifically, a knowledge refreshing scheme is incorporated with the knowledge rehearsal mechanism to enable bi-directional knowledge transfer by introducing a dynamic memory model and an adaptive working model. Moreover, a knowledge consolidation scheme operating on the dual space further improves model stability over the long term. Extensive evaluations show KRC's superiority over the state-of-the-art LReID methods on challenging pedestrian benchmarks.

本文研究持续学习（CL）的逐步学习（CIL）。已经提出了许多方法来处理CIL中的灾难性遗忘（CF）。大多数方法都会为单个头网络中所有任务的所有类别构建单个分类器。为了防止CF，一种流行的方法是记住以前任务中的少数样本，并在培训新任务时重播它们。但是，这种方法仍然患有严重的CF，因为在内存中仅使用有限的保存样本数量来更新或调整了先前任务的参数。本文提出了一种完全不同的方法，该方法使用变压器网络为每个任务（称为多头模型）构建一个单独的分类器（头部），称为更多。与其在内存中使用保存的样本在现有方法中更新以前的任务/类的网络，不如利用保存的样本来构建特定任务分类器（添加新的分类头），而无需更新用于先前任务/类的网络。新任务的模型经过培训，可以学习任务的类别，并且还可以检测到不是从相同数据分布（即，均分布（OOD））的样本。这使测试实例属于的任务的分类器能够为正确的类产生高分，而其他任务的分类器可以产生低分，因为测试实例不是来自这些分类器的数据分布。实验结果表明，更多的表现优于最先进的基线，并且自然能够在持续学习环境中进行OOD检测。

班级增量学习（CIL）吸引了很多关注，但是大多数现有相关的作品都集中在微调整个表示模型上，这不可避免地导致了许多灾难性的遗忘。相比之下，使用语义丰富的预训练的表示模型，参数 - 辅助调整（PAT）仅更改很少的参数来学习新的视觉概念。最近的研究证明，基于PAT的CIL自然可以避免像大多数现有方法一样通过重播或蒸馏而战斗。但是，我们发现基于PAT的CIL仍然面临严重的语义漂移，这是由分类器学习偏见在不同学习阶段引起的问题，这大大降低了基于PAT的CIL的性能。为了解决这个问题，我们提出了增量原型调整（IPT），这是一种简单但有效的方法，它调整了分类和学习示例原型的类别原型，以补偿语义漂移。广泛的实验表明，我们的方法可以有效地补偿语义漂移。与经过良好训练的VIT骨架和其他PAT方法相结合，IPT超过了主流学习基准的最新基准。

Conventionally, deep neural networks are trained offline, relying on a large dataset prepared in advance. This paradigm is often challenged in real-world applications, e.g. online services that involve continuous streams of incoming data. Recently, incremental learning receives increasing attention, and is considered as a promising solution to the practical challenges mentioned above. However, it has been observed that incremental learning is subject to a fundamental difficulty -catastrophic forgetting, namely adapting a model to new data often results in severe performance degradation on previous tasks or classes. Our study reveals that the imbalance between previous and new data is a crucial cause to this problem. In this work, we develop a new framework for incrementally learning a unified classifier, i.e. a classifier that treats both old and new classes uniformly. Specifically, we incorporate three components, cosine normalization, less-forget constraint, and inter-class separation, to mitigate the adverse effects of the imbalance. Experiments show that the proposed method can effectively rebalance the training process, thus obtaining superior performance compared to the existing methods. On CIFAR-100 and ImageNet, our method can reduce the classification errors by more than 6% and 13% respectively, under the incremental setting of 10 phases.

传统的检测网络通常需要丰富的标记训练样本，而人类可以只有几个例子逐步学习新概念。本文侧重于更具挑战性，而是逼真的类渐进的少量对象检测问题（IFSD）。它旨在逐渐逐渐地将新型对象的模型转移到几个注释的样本中，而不会灾难性地忘记先前学识的样本。为了解决这个问题，我们提出了一种新的方法，最小的方法可以减少遗忘，更少的培训资源和更强的转移能力。具体而言，我们首先介绍转移策略，以减少不必要的重量适应并改善IFSD的传输能力。在此基础上，我们使用较少的资源消耗方法整合知识蒸馏技术来缓解遗忘，并提出基于新的基于聚类的示例选择过程，以保持先前学习的更多辨别特征。作为通用且有效的方法，最多可以在很大程度上提高各种基准测试的IFSD性能。

在这个不断变化的世界中，必须不断学习新概念的能力。但是，深层神经网络在学习新类别时会遭受灾难性的遗忘。已经提出了许多减轻这种现象的作品，而其中大多数要么属于稳定性困境，要么陷入了过多的计算或储存开销。受到梯度增强算法的启发，以逐渐适应目标模型和上一个合奏模型之间的残差，我们提出了一种新颖的两阶段学习范式寄养物，使该模型能够适应新的类别。具体而言，我们首先动态扩展新模块，以适合原始模型的目标和输出之间的残差。接下来，我们通过有效的蒸馏策略删除冗余参数和特征尺寸，以维护单个骨干模型。我们在不同的设置下验证CIFAR-100和Imagenet-100/1000的方法寄养。实验结果表明，我们的方法实现了最先进的性能。代码可在以下网址获得：https：//github.com/g-u-n/eccv22-foster。

深度学习模型在逐步学习新任务时遭受灾难性遗忘。已经提出了增量学习，以保留旧课程的知识，同时学习识别新课程。一种典型的方法是使用一些示例来避免忘记旧知识。在这种情况下，旧类和新课之间的数据失衡是导致模型性能下降的关键问题。由于数据不平衡，已经设计了几种策略来纠正新类别的偏见。但是，他们在很大程度上依赖于新旧阶层之间偏见关系的假设。因此，它们不适合复杂的现实世界应用。在这项研究中，我们提出了一种假设不足的方法，即多粒性重新平衡（MGRB），以解决此问题。重新平衡方法用于减轻数据不平衡的影响；但是，我们从经验上发现，他们将拟合新的课程。为此，我们进一步设计了一个新颖的多晶正式化项，该项使模型还可以考虑除了重新平衡数据之外的类别的相关性。类层次结构首先是通过将语义或视觉上类似类分组来构建的。然后，多粒性正则化将单热标签向量转换为连续的标签分布，这反映了基于构造的类层次结构的目标类别和其他类之间的关系。因此，该模型可以学习类间的关系信息，这有助于增强新旧课程的学习。公共数据集和现实世界中的故障诊断数据集的实验结果验证了所提出的方法的有效性。

很少有人提出了几乎没有阶级的课程学习（FSCIL），目的是使深度学习系统能够逐步学习有限的数据。最近，一位先驱声称，通常使用的基于重播的课堂学习方法（CIL）是无效的，因此对于FSCIL而言并不是首选。如果真理，这对FSCIL领域产生了重大影响。在本文中，我们通过经验结果表明，采用数据重播非常有利。但是，存储和重播旧数据可能会导致隐私问题。为了解决此问题，我们或建议使用无数据重播，该重播可以通过发电机综合数据而无需访问真实数据。在观察知识蒸馏的不确定数据的有效性时，我们在发电机培训中强加了熵正则化，以鼓励更不确定的例子。此外，我们建议使用单速样标签重新标记生成的数据。这种修改使网络可以通过完全减少交叉渗透损失来学习，从而减轻了在常规知识蒸馏方法中平衡不同目标的问题。最后，我们对CIFAR-100，Miniimagenet和Cub-200展示了广泛的实验结果和分析，以证明我们提出的效果。

Continual Learning (CL) is a field dedicated to devise algorithms able to achieve lifelong learning. Overcoming the knowledge disruption of previously acquired concepts, a drawback affecting deep learning models and that goes by the name of catastrophic forgetting, is a hard challenge. Currently, deep learning methods can attain impressive results when the data modeled does not undergo a considerable distributional shift in subsequent learning sessions, but whenever we expose such systems to this incremental setting, performance drop very quickly. Overcoming this limitation is fundamental as it would allow us to build truly intelligent systems showing stability and plasticity. Secondly, it would allow us to overcome the onerous limitation of retraining these architectures from scratch with the new updated data. In this thesis, we tackle the problem from multiple directions. In a first study, we show that in rehearsal-based techniques (systems that use memory buffer), the quantity of data stored in the rehearsal buffer is a more important factor over the quality of the data. Secondly, we propose one of the early works of incremental learning on ViTs architectures, comparing functional, weight and attention regularization approaches and propose effective novel a novel asymmetric loss. At the end we conclude with a study on pretraining and how it affects the performance in Continual Learning, raising some questions about the effective progression of the field. We then conclude with some future directions and closing remarks.

Continual Learning (CL) is an emerging machine learning paradigm that aims to learn from a continuous stream of tasks without forgetting knowledge learned from the previous tasks. To avoid performance decrease caused by forgetting, prior studies exploit episodic memory (EM), which stores a subset of the past observed samples while learning from new non-i.i.d. data. Despite the promising results, since CL is often assumed to execute on mobile or IoT devices, the EM size is bounded by the small hardware memory capacity and makes it infeasible to meet the accuracy requirements for real-world applications. Specifically, all prior CL methods discard samples overflowed from the EM and can never retrieve them back for subsequent training steps, incurring loss of information that would exacerbate catastrophic forgetting. We explore a novel hierarchical EM management strategy to address the forgetting issue. In particular, in mobile and IoT devices, real-time data can be stored not just in high-speed RAMs but in internal storage devices as well, which offer significantly larger capacity than the RAMs. Based on this insight, we propose to exploit the abundant storage to preserve past experiences and alleviate the forgetting by allowing CL to efficiently migrate samples between memory and storage without being interfered by the slow access speed of the storage. We call it Carousel Memory (CarM). As CarM is complementary to existing CL methods, we conduct extensive evaluations of our method with seven popular CL methods and show that CarM significantly improves the accuracy of the methods across different settings by large margins in final average accuracy (up to 28.4%) while retaining the same training efficiency.

大多数元学习方法都假设存在于可用于基本知识的情节元学习的一组非常大的标记数据。这与更现实的持续学习范例形成对比，其中数据以包含不相交类的任务的形式逐步到达。在本文中，我们考虑了这个增量元学习（IML）的这个问题，其中类在离散任务中逐步呈现。我们提出了一种方法，我们调用了IML，我们称之为eCISODIC重播蒸馏（ERD），该方法将来自当前任务的类混合到当前任务中，当研究剧集时，来自先前任务的类别示例。然后将这些剧集用于知识蒸馏以最大限度地减少灾难性的遗忘。四个数据集的实验表明ERD超越了最先进的。特别是，在一次挑战的单次次数较挑战，长任务序列增量元学习场景中，我们将IML和联合训练与当前状态的3.5％/ 10.1％/ 13.4％之间的差距降低我们在Diered-ImageNet / Mini-ImageNet / CIFAR100上分别为2.6％/ 2.9％/ 5.0％。

在从少数类（基类）开始的情况下，已经广泛研究了课堂学习学习（CIL）。取而代之的是，我们探索了一个研究不足的CIL现实环境，该设置是从在大量基类中进行预训练的强大模型开始。我们假设强大的基本模型可以为新颖的类别提供良好的表示，并且可以通过小型适应来进行渐进的学习。我们提出了一个2阶段的训练方案，i）功能增强 - 将部分的克隆部分克隆并在新型数据上进行微调，ii）融合 - 将基础和新型分类器组合到统一的分类器中。实验表明，所提出的方法在大型成像网数据集上的最先进的CIL方法明显优于最先进的CIL方法（例如，总体准确度 +最佳 +最佳精度为10％）。我们还建议和分析研究研究的实际CIL方案，例如与分布转移的基础新颖性重叠。我们提出的方法是鲁棒的，并概括了所有分析的CIL设置。代码可从https://github.com/amazon-research/sp-cil获得。

General Continual Learning (GCL) aims at learning from non independent and identically distributed stream data without catastrophic forgetting of the old tasks that don't rely on task boundaries during both training and testing stages. We reveal that the relation and feature deviations are crucial problems for catastrophic forgetting, in which relation deviation refers to the deficiency of the relationship among all classes in knowledge distillation, and feature deviation refers to indiscriminative feature representations. To this end, we propose a Complementary Calibration (CoCa) framework by mining the complementary model's outputs and features to alleviate the two deviations in the process of GCL. Specifically, we propose a new collaborative distillation approach for addressing the relation deviation. It distills model's outputs by utilizing ensemble dark knowledge of new model's outputs and reserved outputs, which maintains the performance of old tasks as well as balancing the relationship among all classes. Furthermore, we explore a collaborative self-supervision idea to leverage pretext tasks and supervised contrastive learning for addressing the feature deviation problem by learning complete and discriminative features for all classes. Extensive experiments on four popular datasets show that our CoCa framework achieves superior performance against state-of-the-art methods. Code is available at https://github.com/lijincm/CoCa.