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

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。

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.

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。

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.

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。

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。

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.

Learning from changing tasks and sequential experience without forgetting the obtained knowledge is a challenging problem for artificial neural networks. In this work, we focus on two challenging problems in the paradigm of Continual Learning (CL) without involving any old data: (i) the accumulation of catastrophic forgetting caused by the gradually fading knowledge space from which the model learns the previous knowledge; (ii) the uncontrolled tug-of-war dynamics to balance the stability and plasticity during the learning of new tasks. In order to tackle these problems, we present Progressive Learning without Forgetting (PLwF) and a credit assignment regime in the optimizer. PLwF densely introduces model functions from previous tasks to construct a knowledge space such that it contains the most reliable knowledge on each task and the distribution information of different tasks, while credit assignment controls the tug-of-war dynamics by removing gradient conflict through projection. Extensive ablative experiments demonstrate the effectiveness of PLwF and credit assignment. In comparison with other CL methods, we report notably better results even without relying on any raw data.

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.

机器学习中的终身学习范式是一个有吸引力的替代方案，不仅是由于其与生物学学习的相似之处，而且它通过避免过度模型重新训练来减少能量浪费的可能性。对此范式的关键挑战是灾难性遗忘的现象。随着在机器学习中训练有素的模型的越来越受欢迎和成功，我们提出了问题：终身学习中的训练前比赛，特别是关于灾难性的遗忘？我们在大型预先训练模型的上下文中调查现有方法，并在各种文本和图像分类任务中评估其性能，包括使用15个不同的NLP任务的新型数据集进行大规模研究。在所有设置中，我们观察到，通用预训练隐含地减轻了在与随机初始化模型相比依次学习多个任务时灾难性忘记的影响。然后，我们进一步调查为什么预先训练缓解在这个环境中忘记。我们通过分析损失景观来研究这种现象，发现预先训练的重量似乎可以通过导致更宽的最小值来缓解遗忘。基于这一洞察力，我们提出了对当前任务损失和损失盆地锐利的共同优化，以便在连续微调期间明确鼓励更广泛的盆地。我们表明，这种优化方法导致与跨多个设置的任务顺序持续学习的性能相当，而无需保留具有任务数量的大小的内存。

Catastrophic forgetting occurs when a neural network loses the information learned in a previous task after training on subsequent tasks. This problem remains a hurdle for artificial intelligence systems with sequential learning capabilities. In this paper, we propose a task-based hard attention mechanism that preserves previous tasks' information without affecting the current task's learning. A hard attention mask is learned concurrently to every task, through stochastic gradient descent, and previous masks are exploited to condition such learning. We show that the proposed mechanism is effective for reducing catastrophic forgetting, cutting current rates by 45 to 80%. We also show that it is robust to different hyperparameter choices, and that it offers a number of monitoring capabilities. The approach features the possibility to control both the stability and compactness of the learned knowledge, which we believe makes it also attractive for online learning or network compression applications.

持续学习的目标（CL）是随着时间的推移学习不同的任务。与CL相关的主要Desiderata是在旧任务上保持绩效，利用后者来改善未来任务的学习，并在培训过程中引入最小的开销（例如，不需要增长的模型或再培训）。我们建议通过固定密度的稀疏神经网络来解决这些避难所的神经启发性塑性适应（NISPA）体系结构。 NISPA形成了稳定的途径，可以从较旧的任务中保存知识。此外，NISPA使用连接重新设计来创建新的塑料路径，以重用有关新任务的现有知识。我们对EMNIST，FashionMnist，CIFAR10和CIFAR100数据集的广泛评估表明，NISPA的表现明显胜过代表性的最先进的持续学习基线，并且与盆地相比，它的可学习参数最多少了十倍。我们还认为稀疏是持续学习的重要组成部分。 NISPA代码可在https://github.com/burakgurbuz97/nispa上获得。

当代理在终身学习设置中遇到连续的新任务流时，它利用了从早期任务中获得的知识来帮助更好地学习新任务。在这种情况下，确定有效的知识表示成为一个具有挑战性的问题。大多数研究工作都建议将过去任务中的一部分示例存储在重播缓冲区中，将一组参数集成给每个任务，或通过引入正则化项来对参数进行过多的更新。尽管现有方法采用了一般任务无关的随机梯度下降更新规则，但我们提出了一个任务吸引的优化器，可根据任务之间的相关性调整学习率。我们通过累积针对每个任务的梯度来利用参数在更新过程中采取的方向。这些基于任务的累积梯度充当了在整个流中维护和更新的知识库。我们从经验上表明，我们提出的自适应学习率不仅说明了灾难性的遗忘，而且还允许积极的向后转移。我们还表明，在具有大量任务的复杂数据集中，我们的方法比终身学习中的几种最先进的方法更好。

模块化是持续学习（CL）的令人信服的解决方案，是相关任务建模的问题。学习和组合模块来解决不同的任务提供了一种抽象来解决CL的主要挑战，包括灾难性的遗忘，向后和向前传输跨任务以及子线性模型的增长。我们引入本地模块组成（LMC），该方法是模块化CL的方法，其中每个模块都提供了局部结构组件，其估计模块与输入的相关性。基于本地相关评分进行动态模块组合。我们展示了对任务身份（IDS）的不可知性来自（本地）结构学习，该结构学习是特定于模块和/或模型特定于以前的作品，使LMC适用于与以前的作品相比的更多CL设置。此外，LMC还跟踪输入分布的统计信息，并在检测到异常样本时添加新模块。在第一组实验中，LMC与最近的持续转移学习基准上的现有方法相比，不需要任务标识。在另一个研究中，我们表明结构学习的局部性允许LMC插入相关但未遵守的任务（OOD），以及在不同任务序列上独立于不同的任务序列培训的模块化网络，而无需任何微调。最后，在寻找LMC的限制，我们在30和100个任务的更具挑战性序列上研究它，展示了本地模块选择在存在大量候选模块时变得更具挑战性。在此设置中，与Oracle基准的基线相比，最佳执行LMC产生的模块更少，但它达到了较低的总体精度。 CodeBase可在https://github.com/oleksost/lmc下找到。

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.

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.

从一系列任务中学习一生对于人为一般情报的代理至关重要。这要求代理商不断学习和记住没有干扰的新知识。本文首先展示了使用神经网络的终身学习的基本问题，命名为Anterograde忘记，即保留和转移记忆可能会抑制新知识的学习。这归因于，由于它不断记住历史知识，因此神经网络的学习能力将减少，并且可能发生概念混淆的事实，因为它转移到当前任务的无关旧知识。这项工作提出了一个名为循环内存网络（CMN）的一般框架，以解决终身学习神经网络中的伪造遗忘。 CMN由两个单独的存储器网络组成，用于存储短期和长期存储器以避免容量收缩。传输单元被设计为连接这两个存储器网络，使得从长期存储器网络的知识转移到短期内存网络以减轻概念混淆，并且开发了存储器整合机制以将短期知识集成到其中知识累积的长期记忆网络。实验结果表明，CMN可以有效地解决了在几个与任务相关的，任务冲突，类增量和跨域基准测试中忘记的伪造遗忘。

A growing body of research in continual learning focuses on the catastrophic forgetting problem. While many attempts have been made to alleviate this problem, the majority of the methods assume a single model in the continual learning setup. In this work, we question this assumption and show that employing ensemble models can be a simple yet effective method to improve continual performance. However, ensembles' training and inference costs can increase significantly as the number of models grows. Motivated by this limitation, we study different ensemble models to understand their benefits and drawbacks in continual learning scenarios. Finally, to overcome the high compute cost of ensembles, we leverage recent advances in neural network subspace to propose a computationally cheap algorithm with similar runtime to a single model yet enjoying the performance benefits of ensembles.