对于新应用程序，例如家庭机器人，智能手机的用户个性化以及增强/虚拟现实耳机，需要实时的持续学习持续学习。但是，此设置构成了独特的挑战：嵌入式设备的内存和计算能力有限，并且在非平稳数据流进行更新时，灾难性遗忘的常规机器学习模型会遭受损失。尽管已经开发了几种在线持续学习模型，但它们对嵌入式应用程序的有效性尚未进行严格研究。在本文中，我们首先确定在线持续学习者必须满足以有效执行实时，设备学习的标准。然后，当与移动神经网络一起使用时，我们研究了几种在线连续学习方法的功效。我们衡量他们的性能，内存使用情况，计算要求以及将其推广到分类外输入的能力。

现代ML方法在培训数据是IID，大规模和良好标记的时候Excel。在不太理想的条件下学习仍然是一个开放的挑战。在不利条件下，几次射击，持续的，转移和代表学习的子场在学习中取得了很大的进步;通过方法和见解，每个都提供了独特的优势。这些方法解决了不同的挑战，例如依次到达的数据或稀缺的训练示例，然而，在部署之前，ML系统将面临困难的条件。因此，需要可以处理实际设置中许多学习挑战的一般ML系统。为了促进一般ML方法目标的研究，我们介绍了一个新的统一评估框架 - 流体（灵活的顺序数据）。流体集成了几次拍摄，持续的，转移和表示学习的目标，同时能够比较和整合这些子场的技术。在流体中，学习者面临数据流，并且必须在选择如何更新自身时进行顺序预测，快速调整到新颖的类别，并处理更改的数据分布;虽然会计计算总额。我们对广泛的方法进行实验，这些方法阐述了新的洞察当前解决方案的优缺点并表明解决了新的研究问题。作为更一般方法的起点，我们展示了两种新的基线，其在流体上优于其他评估的方法。项目页面：https：//raivn.cs.washington.edu/projects/fluid/。

Continual Learning, also known as Lifelong or Incremental Learning, has recently gained renewed interest among the Artificial Intelligence research community. Recent research efforts have quickly led to the design of novel algorithms able to reduce the impact of the catastrophic forgetting phenomenon in deep neural networks. Due to this surge of interest in the field, many competitions have been held in recent years, as they are an excellent opportunity to stimulate research in promising directions. This paper summarizes the ideas, design choices, rules, and results of the challenge held at the 3rd Continual Learning in Computer Vision (CLVision) Workshop at CVPR 2022. The focus of this competition is the complex continual object detection task, which is still underexplored in literature compared to classification tasks. The challenge is based on the challenge version of the novel EgoObjects dataset, a large-scale egocentric object dataset explicitly designed to benchmark continual learning algorithms for egocentric category-/instance-level object understanding, which covers more than 1k unique main objects and 250+ categories in around 100k video frames.

Artificial neural networks thrive in solving the classification problem for a particular rigid task, acquiring knowledge through generalized learning behaviour from a distinct training phase. The resulting network resembles a static entity of knowledge, with endeavours to extend this knowledge without targeting the original task resulting in a catastrophic forgetting. Continual learning shifts this paradigm towards networks that can continually accumulate knowledge over different tasks without the need to retrain from scratch. We focus on task incremental classification, where tasks arrive sequentially and are delineated by clear boundaries. Our main contributions concern (1) a taxonomy and extensive overview of the state-of-the-art; (2) a novel framework to continually determine the stability-plasticity trade-off of the continual learner; (3) a comprehensive experimental comparison of 11 state-of-the-art continual learning methods and 4 baselines. We empirically scrutinize method strengths and weaknesses on three benchmarks, considering Tiny Imagenet and large-scale unbalanced iNaturalist and a sequence of recognition datasets. We study the influence of model capacity, weight decay and dropout regularization, and the order in which the tasks are presented, and qualitatively compare methods in terms of required memory, computation time and storage.

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.

先前的关于自我监督预训练的研究重点是联合培训方案，在该场景中，假定大量未标记的数据一次性地将其作为输入，只有那时才受过培训的学习者。不幸的是，这种问题设置通常是不切实际的，即使不是不可行的，因为许多现实世界的任务依赖于顺序学习，例如，数据是以流方式分散或收集的。在本文中，我们对通过流数据进行了对自我监督的预训练进行了首次彻底而专门的研究，旨在阐明这种被忽视的设置下的模型行为。具体而言，我们在来自ImageNet和域内的四类预训练流数据数据上预先培训超过500个模型，并在三种类型的下游任务和12个不同的下游数据集上对其进行评估。我们的研究表明，以某种方式超出了我们的期望，通过简单的数据重播或参数正则化，顺序的自我监督预训练的预训练证明是联合预训练的有效替代方法，因为前者的性能主要与这些培训相同后者。此外，灾难性的遗忘是顺序监督学习中的一个常见问题，在顺序的自学学习（SSL）中得到了极大的缓解，这是通过我们对损失景观中最小值的表示和敏锐度的全面经验分析来很好地证明的。因此，我们的发现表明，在实践中，对于SSL，可以主要通过顺序学习来代替繁琐的联合培训，这反过来又可以更广泛的潜在应用方案。

根据互补学习系统（CLS）理论〜\ cite {mcclelland1995there}在神经科学中，人类通过两个补充系统有效\ emph {持续学习}：一种快速学习系统，以海马为中心，用于海马，以快速学习细节，个人体验，个人体验，个人体验，个人体验，个人体验，个人体验，个人体验，个人体验的快速学习， ;以及位于新皮层中的缓慢学习系统，以逐步获取有关环境的结构化知识。在该理论的激励下，我们提出\ emph {dualnets}（对于双网络），这是一个一般的持续学习框架，该框架包括一个快速学习系统，用于监督从特定任务和慢速学习系统中的模式分离代表学习，用于表示任务的慢学习系统 - 不可知论的一般代表通过自我监督学习（SSL）。双网符可以无缝地将两种表示类型纳入整体框架中，以促进在深层神经网络中更好地持续学习。通过广泛的实验，我们在各种持续的学习协议上展示了双网络的有希望的结果，从标准离线，任务感知设置到具有挑战性的在线，无任务的场景。值得注意的是，在Ctrl〜 \ Cite {veniat2020202020202020202020202020202020202020202020202020202020202021- coite {ostapenko2021-continual}的基准中。此外，我们进行了全面的消融研究，以验证双nets功效，鲁棒性和可伸缩性。代码可在\ url {https://github.com/phquang/dualnet}上公开获得。

Lack of performance when it comes to continual learning over non-stationary distributions of data remains a major challenge in scaling neural network learning to more human realistic settings. In this work we propose a new conceptualization of the continual learning problem in terms of a temporally symmetric trade-off between transfer and interference that can be optimized by enforcing gradient alignment across examples. We then propose a new algorithm, Meta-Experience Replay (MER), that directly exploits this view by combining experience replay with optimization based meta-learning. This method learns parameters that make interference based on future gradients less likely and transfer based on future gradients more likely. 1 We conduct experiments across continual lifelong supervised learning benchmarks and non-stationary reinforcement learning environments demonstrating that our approach consistently outperforms recently proposed baselines for continual learning. Our experiments show that the gap between the performance of MER and baseline algorithms grows both as the environment gets more non-stationary and as the fraction of the total experiences stored gets smaller.

在不忘记以前的任务的情况下不断获得新知识的能力仍然是计算机视觉系统的具有挑战性问题。标准的持续学习基准专注于在离线设置中从静态IID图像学习。在这里，我们研究了一个更具挑战性和现实的在线持续学习问题，称为在线流学习。像人类一样，一些AI代理必须从连续的不重复数据流逐步学习。我们提出了一种新颖的模型，假设驱动的增强存储器网络（HAMN），其有效地使用“假设”的增强内存矩阵来巩固先前的知识，并重播重建的图像特征以避免灾难性的遗忘。与像素级和生成的重播方法相比，Hamn的优点是两倍。首先，基于假设的知识合并避免了图像像素空间中的冗余信息，并使内存使用更有效。其次，增强记忆中的假设可以重新用于学习新任务，提高泛化和转移学习能力。鉴于视频流缺乏在线增量类学习数据集，我们介绍并调整两个额外的视频数据集，Toybox和Ilab，用于在线流学习。我们还在Core50和在线CIFAR100数据集上评估我们的方法。我们的方法显着优于所有最先进的方法，同时提供更有效的内存使用情况。所有源代码和数据都在https://github.com/kreimanlab/augmem公开使用

我们展示了一个通过ImageNet（Optip）问题，旨在研究流媒体环境中深度学习的有效性。 Imagenet是一个广泛的已知基准数据集，有助于推动和评估深度学习的最新进步。通常，深度学习方法训练在模型具有随机访问的静态数据上，使用多次通过数据集，在每个时期的训练中随机随机抽搐。这种数据访问假设在许多真实情景中不存在，其中从流中收集大规模数据并存储和访问所有数据由于存储成本和隐私问题而变得不切实际。对于拍摄，我们将ImageNet数据视为顺序到达，内存预算有限的内存预算来存储一个小的数据子集。我们观察到，在单次训练中培训一个深度网络，用于多挪训练导致预测准确性的巨大降低。我们表明，尽管对典型的连续问题设置不同，但通过支付小的记忆成本和利用为持续学习的技术来说，可以显着降低性能差距。我们建议使用参考学习资源有效的深度学习。

持续学习和少数学习是追求改善机器学习的重要领域。每个边界的工作越来越多，但将两者结合起来很少。但是最近，Antoniou等人。 ARXIV：2004.11967引入了一个连续的少数学习框架CFSL，将两者都结合在一起。在这项研究中，我们扩展了CFSL，以使其与标准持续学习实验更具可比性，通常会介绍更多的类。我们还引入了一个“实例测试”以对非常相似的特定实例进行分类 - ML通常忽略的动物认知能力。我们从原始CFSL工作中选择了代表性的基线模型，并将其与具有海马启发性重播的模型进行了比较，因为海马被认为对动物中的这种学习至关重要。正如预期的那样，学习更多的课程比原始的CFSL实验更加困难，有趣的是，它们的呈现方式对性能有所不同。实例测试中的准确性与分类任务相当。使用重播进行合并可改善两种类型的任务的性能，尤其是实例测试。

The ability to dynamically adapt neural networks to newly-available data without performance deterioration would revolutionize deep learning applications. Streaming learning (i.e., learning from one data example at a time) has the potential to enable such real-time adaptation, but current approaches i) freeze a majority of network parameters during streaming and ii) are dependent upon offline, base initialization procedures over large subsets of data, which damages performance and limits applicability. To mitigate these shortcomings, we propose Cold Start Streaming Learning (CSSL), a simple, end-to-end approach for streaming learning with deep networks that uses a combination of replay and data augmentation to avoid catastrophic forgetting. Because CSSL updates all model parameters during streaming, the algorithm is capable of beginning streaming from a random initialization, making base initialization optional. Going further, the algorithm's simplicity allows theoretical convergence guarantees to be derived using analysis of the Neural Tangent Random Feature (NTRF). In experiments, we find that CSSL outperforms existing baselines for streaming learning in experiments on CIFAR100, ImageNet, and Core50 datasets. Additionally, we propose a novel multi-task streaming learning setting and show that CSSL performs favorably in this domain. Put simply, CSSL performs well and demonstrates that the complicated, multi-step training pipelines adopted by most streaming methodologies can be replaced with a simple, end-to-end learning approach without sacrificing performance.

大规模预训练的快速开发导致基础模型可以充当各种下游任务和领域的有效提取器。在此激励的情况下，我们研究了预训练的视觉模型的功效，作为下游持续学习（CL）场景的基础。我们的目标是双重的。首先，我们想了解RAW-DATA空间中CL和预训练编码器的潜在空间之间CL之间的计算准确性权衡。其次，我们研究编码器的特征，训练算法和数据以及所得的潜在空间如何影响CL性能。为此，我们将各种预训练的模型在大规模基准测试方案中的功效与在潜在和原始数据空间中应用的香草重播设置的功效。值得注意的是，这项研究表明了转移，遗忘，任务相似性和学习如何取决于输入数据特征，而不一定取决于CL算法。首先，我们表明，在某些情况下，通过可忽略的计算中的非参数分类器可以很容易地实现合理的CL性能。然后，我们展示模型如何在更广泛的数据上进行预训练，从而为各种重播大小提供更好的性能。我们以这些表示形式的代表性相似性和传递属性来解释这一点。最后，与训练域相比，我们显示了自我监督预训练对下游域的有效性。我们指出并验证了几个研究方向，这些方向可以进一步提高潜在CL的功效，包括表示结合。本研究中使用的各种数据集可以用作进一步CL研究的计算效率游乐场。该代码库可在https://github.com/oleksost/latent_cl下获得。

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).

我们研究深度神经网络中不同的输出层如何学习并忘记在持续的学习环境中。以下三个因素可能会影响输出层中的灾难性忘记：（1）权重修改，（2）干扰和（3）投影漂移。在本文中，我们的目标是提供更多关于如何改变输出层可以解决（1）和（2）的洞察。在几个连续学习情景中提出并评估了这些问题的一些潜在解决方案。我们表明，最佳执行类型的输出层取决于数据分布漂移和/或可用数据量。特别地，在某些情况下，在某些情况下，标准线性层将失败，结果改变参数化是足够的，以便实现显着更好的性能，从而引入持续学习算法，而是使用标准SGD训练模型。我们的分析和结果在连续学习场景中输出层动态的阐明，并表明了一种选择给定场景的最佳输出层的方法。

The literature on machine learning in the context of data streams is vast and growing. However, many of the defining assumptions regarding data-stream learning tasks are too strong to hold in practice, or are even contradictory such that they cannot be met in the contexts of supervised learning. Algorithms are chosen and designed based on criteria which are often not clearly stated, for problem settings not clearly defined, tested in unrealistic settings, and/or in isolation from related approaches in the wider literature. This puts into question the potential for real-world impact of many approaches conceived in such contexts, and risks propagating a misguided research focus. We propose to tackle these issues by reformulating the fundamental definitions and settings of supervised data-stream learning with regard to contemporary considerations of concept drift and temporal dependence; and we take a fresh look at what constitutes a supervised data-stream learning task, and a reconsideration of algorithms that may be applied to tackle such tasks. Through and in reflection of this formulation and overview, helped by an informal survey of industrial players dealing with real-world data streams, we provide recommendations. Our main emphasis is that learning from data streams does not impose a single-pass or online-learning approach, or any particular learning regime; and any constraints on memory and time are not specific to streaming. Meanwhile, there exist established techniques for dealing with temporal dependence and concept drift, in other areas of the literature. For the data streams community, we thus encourage a shift in research focus, from dealing with often-artificial constraints and assumptions on the learning mode, to issues such as robustness, privacy, and interpretability which are increasingly relevant to learning in data streams in academic and industrial settings.

Continual Learning is a step towards lifelong intelligence where models continuously learn from recently collected data without forgetting previous knowledge. Existing continual learning approaches mostly focus on image classification in the class-incremental setup with clear task boundaries and unlimited computational budget. This work explores Online Domain-Incremental Continual Segmentation~(ODICS), a real-world problem that arises in many applications, \eg, autonomous driving. In ODICS, the model is continually presented with batches of densely labeled images from different domains; computation is limited and no information about the task boundaries is available. In autonomous driving, this may correspond to the realistic scenario of training a segmentation model over time on a sequence of cities. We analyze several existing continual learning methods and show that they do not perform well in this setting despite working well in class-incremental segmentation. We propose SimCS, a parameter-free method complementary to existing ones that leverages simulated data as a continual learning regularizer. Extensive experiments show consistent improvements over different types of continual learning methods that use regularizers and even replay.

人类的持续学习（CL）能力与稳定性与可塑性困境密切相关，描述了人类如何实现持续的学习能力和保存的学习信息。自发育以来，CL的概念始终存在于人工智能（AI）中。本文提出了对CL的全面审查。与之前的评论不同，主要关注CL中的灾难性遗忘现象，本文根据稳定性与可塑性机制的宏观视角来调查CL。类似于生物对应物，“智能”AI代理商应该是I）记住以前学到的信息（信息回流）; ii）不断推断新信息（信息浏览:); iii）转移有用的信息（信息转移），以实现高级CL。根据分类学，评估度量，算法，应用以及一些打开问题。我们的主要贡献涉及I）从人工综合情报层面重新检查CL; ii）在CL主题提供详细和广泛的概述; iii）提出一些关于CL潜在发展的新颖思路。

持续学习（CL）旨在开发单一模型适应越来越多的任务的技术，从而潜在地利用跨任务的学习以资源有效的方式。 CL系统的主要挑战是灾难性的遗忘，在学习新任务时忘记了早期的任务。为了解决此问题，基于重播的CL方法在遇到遇到任务中选择的小缓冲区中维护和重复培训。我们提出梯度Coreset重放（GCR），一种新颖的重播缓冲区选择和使用仔细设计的优化标准的更新策略。具体而言，我们选择并维护一个“Coreset”，其与迄今为止关于当前模型参数的所有数据的梯度紧密近似，并讨论其有效应用于持续学习设置所需的关键策略。在学习的离线持续学习环境中，我们在最先进的最先进的最先进的持续学习环境中表现出显着的收益（2％-4％）。我们的调查结果还有效地转移到在线/流媒体CL设置，从而显示现有方法的5％。最后，我们展示了持续学习的监督对比损失的价值，当与我们的子集选择策略相结合时，累计增益高达5％。

物联网系统中的微型机器学习（Tinyml）利用MCU作为数据处理的边缘设备。但是，传统的Tinyml方法只能执行推理，仅限于静态环境或类。真实情况通常在动态环境中起作用，从而将原始神经模型不再合适的上下文漂移。因此，预训练的模型降低了其一生中的准确性和可靠性，因为记录的数据缓慢变为过时或出现新模式。连续学习策略通过对参数进行运行时进行微调维护模型。本文比较了两个实际应用中的四种最新算法：i）基于加速度计数据的手势识别和ii）图像分类。我们的结果证实了这些系统的可靠性以及将它们部署到微小的MCUS中的可行性，相对于无约束计算平台的原始模型的精确度下降了几个百分点。