While deep learning has led to remarkable advances across diverse applications, it struggles in domains where the data distribution changes over the course of learning. In stark contrast, biological neural networks continually adapt to changing domains, possibly by leveraging complex molecular machinery to solve many tasks simultaneously. In this study, we introduce intelligent synapses that bring some of this biological complexity into artificial neural networks. Each synapse accumulates task relevant information over time, and exploits this information to rapidly store new memories without forgetting old ones. We evaluate our approach on continual learning of classification tasks, and show that it dramatically reduces forgetting while maintaining computational efficiency.

已知生物制剂在他们的生活过程中学习许多不同的任务，并且能够重新审视以前的任务和行为，而没有表现不损失。相比之下，人工代理容易出于“灾难性遗忘”，在以前任务上的性能随着所获取的新的任务而恶化。最近使用该方法通过鼓励参数保持接近以前任务的方法来解决此缺点。这可以通过（i）使用特定的参数正常数来完成，该参数正常数是在参数空间中映射合适的目的地，或（ii）通过将渐变投影到不会干扰先前任务的子空间来指导优化旅程。然而，这些方法通常在前馈和经常性神经网络中表现出子分子表现，并且经常性网络对支持生物持续学习的神经动力学研究感兴趣。在这项工作中，我们提出了自然的持续学习（NCL），一种统一重量正则化和预测梯度下降的新方法。 NCL使用贝叶斯重量正常化来鼓励在收敛的所有任务上进行良好的性能，并将其与梯度投影结合使用先前的精度，这可以防止在优化期间陷入灾难性遗忘。当应用于前馈和经常性网络中的连续学习问题时，我们的方法占据了标准重量正则化技术和投影的方法。最后，训练有素的网络演变了特定于任务特定的动态，这些动态被认为是学习的新任务，类似于生物电路中的实验结果。

Incremental learning (IL) has received a lot of attention recently, however, the literature lacks a precise problem definition, proper evaluation settings, and metrics tailored specifically for the IL problem. One of the main objectives of this work is to fill these gaps so as to provide a common ground for better understanding of IL. The main challenge for an IL algorithm is to update the classifier whilst preserving existing knowledge. We observe that, in addition to forgetting, a known issue while preserving knowledge, IL also suffers from a problem we call intransigence, inability of a model to update its knowledge. We introduce two metrics to quantify forgetting and intransigence that allow us to understand, analyse, and gain better insights into the behaviour of IL algorithms. We present RWalk, a generalization of EWC++ (our efficient version of EWC [7]) and Path Integral [26] with a theoretically grounded KL-divergence based perspective. We provide a thorough analysis of various IL algorithms on MNIST and CIFAR-100 datasets. In these experiments, RWalk obtains superior results in terms of accuracy, and also provides a better trade-off between forgetting and intransigence.

The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Neural networks are not, in general, capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks which they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on the MNIST hand written digit dataset and by learning several Atari 2600 games sequentially.

本文研究了在连续学习框架中使用分类网络的固定架构培训深度学习模型的优化算法的新设计。训练数据是非平稳的，非平稳性是由一系列不同的任务施加的。我们首先分析了一个仅在隔离的学习任务的深层模型，并在网络参数空间中识别一个区域，其中模型性能接近恢复的最佳。我们提供的经验证据表明该区域类似于沿收敛方向扩展的锥体。我们研究了融合后优化器轨迹的主要方向，并表明沿着一些顶级主要方向旅行可以迅速将参数带到锥体之外，但其余方向并非如此。我们认为，当参数被限制以保持在训练过程中迄今为止遇到的单个任务的相交中，可以缓解持续学习环境中的灾难性遗忘。基于此观察结果，我们介绍了我们的方向约束优化（DCO）方法，在每个任务中，我们引入一个线性自动编码器以近似其相应的顶部禁止主要方向。然后将它们以正规化术语的形式合并到损失函数中，以便在不忘记的情况下学习即将到来的任务。此外，为了随着任务数量的增加而控制内存的增长，我们提出了一种称为压缩DCO（DCO-comp）的算法的内存效率版本，该版本为存储所有自动编码器的固定大小分配了存储器。我们从经验上证明，与其他基于最新正规化的持续学习方法相比，我们的算法表现出色。

我们引入了一个新的培训范式，该范围对神经网络参数空间进行间隔约束以控制遗忘。当代持续学习（CL）方法从一系列数据流有效地培训神经网络，同时减少灾难性遗忘的负面影响，但它们不能提供任何确保的确保网络性能不会随着时间的流逝而无法控制地恶化。在这项工作中，我们展示了如何通过将模型的持续学习作为其参数空间的持续收缩来遗忘。为此，我们提出了Hypertrectangle训练，这是一种新的训练方法，其中每个任务都由参数空间中的超矩形表示，完全包含在先前任务的超矩形中。这种配方将NP-HARD CL问题降低到多项式时间，同时提供了完全防止遗忘的弹性。我们通过开发Intercontinet（间隔持续学习）算法来验证我们的主张，该算法利用间隔算术来有效地将参数区域建模为高矩形。通过实验结果，我们表明我们的方法在不连续的学习设置中表现良好，而无需存储以前的任务中的数据。

持续学习研究的主要重点领域是通过设计新算法对分布变化更强大的新算法来减轻神经网络中的“灾难性遗忘”问题。尽管持续学习文献的最新进展令人鼓舞，但我们对神经网络的特性有助于灾难性遗忘的理解仍然有限。为了解决这个问题，我们不关注持续的学习算法，而是在这项工作中专注于模型本身，并研究神经网络体系结构对灾难性遗忘的“宽度”的影响，并表明宽度在遗忘遗产方面具有出人意料的显着影响。为了解释这种效果，我们从各个角度研究网络的学习动力学，例如梯度正交性，稀疏性和懒惰的培训制度。我们提供了与不同架构和持续学习基准之间的经验结果一致的潜在解释。

预训练的代表是现代深度学习成功的关键要素之一。但是，现有的关于持续学习方法的作品主要集中在从头开始逐步学习学习模型。在本文中，我们探讨了一个替代框架，以逐步学习，我们不断从预训练的表示中微调模型。我们的方法利用了预训练的神经网络的线性化技术来进行简单有效的持续学习。我们表明，这使我们能够设计一个线性模型，其中将二次参数正则方法作为最佳持续学习策略，同时享受神经网络的高性能。我们还表明，所提出的算法使参数正则化方法适用于类新问题。此外，我们还提供了一个理论原因，为什么在接受跨凝结损失训练的神经网络上，现有的参数空间正则化算法（例如EWC表现不佳）。我们表明，提出的方法可以防止忘记，同时在图像分类任务上实现高连续的微调性能。为了证明我们的方法可以应用于一般的持续学习设置，我们评估了我们在数据收入，任务收入和课堂学习问题方面的方法。

We introduce a conceptually simple and scalable framework for continual learning domains where tasks are learned sequentially. Our method is constant in the number of parameters and is designed to preserve performance on previously encountered tasks while accelerating learning progress on subsequent problems. This is achieved by training a network with two components: A knowledge base, capable of solving previously encountered problems, which is connected to an active column that is employed to efficiently learn the current task. After learning a new task, the active column is distilled into the knowledge base, taking care to protect any previously acquired skills. This cycle of active learning (progression) followed by consolidation (compression) requires no architecture growth, no access to or storing of previous data or tasks, and no task-specific parameters. We demonstrate the progress & compress approach on sequential classification of handwritten alphabets as well as two reinforcement learning domains: Atari games and 3D maze navigation.

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.

由于灾难性的遗忘，计算系统的持续学习是挑战。我们在果蝇嗅觉系统中发现了两个层神经循环，通过独特地组合稀疏编码和关联学习来解决这一挑战。在第一层中，使用稀疏，高尺寸表示来编码气味，这通过激活非重叠神经元的神经元以进行不同气味来减少内存干扰。在第二层中，在学习期间仅修改异味活性神经元和与气味相关的输出神经元之间的突触;冻结其余重量以防止不相关的存储器被覆盖。我们经验和分析显示，这种简单轻型的算法显着提高了不断的学习性能。飞行关联学习算法与经典的Perceptron学习算法引人注目，尽管我们表现出两种修改对于减少灾难性遗忘至关重要。总体而言，果蝇演变了一种有效的终身学习算法，可以转换来自神经科学的电路机制以改善机器计算。

Humans can learn in a continuous manner. Old rarely utilized knowledge can be overwritten by new incoming information while important, frequently used knowledge is prevented from being erased. In artificial learning systems, lifelong learning so far has focused mainly on accumulating knowledge over tasks and overcoming catastrophic forgetting. In this paper, we argue that, given the limited model capacity and the unlimited new information to be learned, knowledge has to be preserved or erased selectively. Inspired by neuroplasticity, we propose a novel approach for lifelong learning, coined Memory Aware Synapses (MAS). It computes the importance of the parameters of a neural network in an unsupervised and online manner. Given a new sample which is fed to the network, MAS accumulates an importance measure for each parameter of the network, based on how sensitive the predicted output function is to a change in this parameter. When learning a new task, changes to important parameters can then be penalized, effectively preventing important knowledge related to previous tasks from being overwritten. Further, we show an interesting connection between a local version of our method and Hebb's rule, which is a model for the learning process in the brain. We test our method on a sequence of object recognition tasks and on the challenging problem of learning an embedding for predicting <subject, predicate, object> triplets. We show state-of-the-art performance and, for the first time, the ability to adapt the importance of the parameters based on unlabeled data towards what the network needs (not) to forget, which may vary depending on test conditions.

Humans and animals have the ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms that together contribute to the development and specialization of our sensorimotor skills as well as to long-term memory consolidation and retrieval. Consequently, lifelong learning capabilities are crucial for computational systems and autonomous agents interacting in the real world and processing continuous streams of information. However, lifelong learning remains a long-standing challenge for machine learning and neural network models since the continual acquisition of incrementally available information from non-stationary data distributions generally leads to catastrophic forgetting or interference. This limitation represents a major drawback for state-of-the-art deep neural network models that typically learn representations from stationary batches of training data, thus without accounting for situations in which information becomes incrementally available over time. In this review, we critically summarize the main challenges linked to lifelong learning for artificial learning systems and compare existing neural network approaches that alleviate, to different extents, catastrophic forgetting. Although significant advances have been made in domain-specific learning with neural networks, extensive research efforts are required for the development of robust lifelong learning on autonomous agents and robots. We discuss well-established and emerging research motivated by lifelong learning factors in biological systems such as structural plasticity, memory replay, curriculum and transfer learning, intrinsic motivation, and multisensory integration.

In this paper we explore whether the fundamental tool of experimental psychology, the behavioral experiment, has the power to generate insight not only into humans and animals, but artificial systems too. We apply the techniques of experimental psychology to investigating catastrophic forgetting in neural networks. We present a series of controlled experiments with two-layer ReLU networks, and exploratory results revealing a new understanding of the behavior of catastrophic forgetting. Alongside our empirical findings, we demonstrate an alternative, behavior-first approach to investigating neural network phenomena.

我们提出了一种有效的正则化战略（CW-TALAR），用于解决持续的学习问题。它使用由在由所有任务共享的底层神经网络的目标层上定义的两个概率分布之间的校准术语，该概率分布在由所有任务共享的底层神经网络的目标层，以及用于建模输出数据表示的克拉米 - WOLD发生器的简单架构。我们的策略在学习新任务时保留了目标层分发，但不需要记住以前的任务的数据集。我们执行涉及几个常见监督框架的实验，该框架证明了CW-TALAR方法的竞争力与一些现有的现有最先进的持续学习模型相比。

从非平稳的输入数据流进行连续/终身学习是智力的基石。尽管在各种应用中表现出色，但深度神经网络仍容易在学习新信息时忘记他们以前学习的信息。这种现象称为“灾难性遗忘”，深深地植根于稳定性困境。近年来，克服深层神经网络中的灾难性遗忘已成为一个积极的研究领域。特别是，基于梯度投射的方法最近在克服灾难性遗忘时表现出了出色的表现。本文提出了基于稀疏性和异质辍学的两种受生物学启发的机制，这些机制在长期的任务上显着提高了持续学习者的表现。我们提出的方法建立在梯度投影内存（GPM）框架上。我们利用神经网络的每一层中的K-获奖者激活来为每个任务执行层次稀疏激活，以及任务间的异质辍学，鼓励网络在不同任务之间使用非重叠的激活模式。此外，我们引入了两个新的基准，用于在分配转移下连续学习，即连续的瑞士卷和Imagenet Superdog-40。最后，我们对我们提出的方法进行了深入的分析，并证明了各种基准持续学习问题的显着性能。

灾难性忘记破坏了深神网络（DNN）在诸如持续学习和终身学习等方案中的有效性。尽管已经提出了几种解决这个问题的方法，但有限的工作解释了为什么这些方法效果很好。本文的目的是更好地解释一种避免灾难性遗忘的普遍使用的技术：二次正则化。我们表明，二次正规化器可以通过在每次训练迭代时插值当前和先前的值来忘记过去的任务。在多次训练迭代中，这种插值操作降低了更重要的模型参数的学习率，从而最大程度地减少了它们的运动。我们的分析还揭示了二次正则化的两个缺点：（a）参数插值对训练超参数的依赖性通常会导致训练不稳定性，并且（b）（b）将较低的重要性分配到更深的层，这通常是DNNS中遗忘的地方。通过对操作顺序的简单修改，我们表明可以轻松避免这些缺点，从而在4.5％降低平均遗忘时的平均准确度增加6.2 \％。我们通过在不同的环境中培训2000多个模型来确认结果的鲁棒性。可在\ url {https://github.com/ekdeepslubana/qrforgetting}上获得代码

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.

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）。

增量任务学习（ITL）是一个持续学习的类别，试图培训单个网络以进行多个任务（一个接一个），其中每个任务的培训数据仅在培训该任务期间可用。当神经网络接受较新的任务培训时，往往会忘记旧任务。该特性通常被称为灾难性遗忘。为了解决此问题，ITL方法使用情节内存，参数正则化，掩盖和修剪或可扩展的网络结构。在本文中，我们提出了一个基于低级别分解的新的增量任务学习框架。特别是，我们表示每一层的网络权重作为几个等级1矩阵的线性组合。为了更新新任务的网络，我们学习一个排名1（或低级别）矩阵，并将其添加到每一层的权重。我们还引入了一个其他选择器向量，该向量将不同的权重分配给对先前任务的低级矩阵。我们表明，就准确性和遗忘而言，我们的方法的表现比当前的最新方法更好。与基于情节的内存和基于面具的方法相比，我们的方法还提供了更好的内存效率。我们的代码将在https://github.com/csiplab/task-increment-rank-update.git上找到。