Continual learning is increasingly sought after in real world machine
learning applications, as it enables learning in a more human-like manner.
Conventional machine learning approaches fail to achieve this, as incrementally
updating the model with non-identically distributed data leads to catastrophic
forgetting, where existing representations are overwritten. Although
traditional continual learning methods have mostly focused on batch learning,
which involves learning from large collections of labeled data sequentially,
this approach is not well-suited for real-world applications where we would
like new data to be integrated directly. This necessitates a paradigm shift
towards streaming learning. In this paper, we propose a streaming version of
regularized discriminant analysis as a solution to this challenge. We combine
our algorithm with a convolutional neural network and demonstrate that it
outperforms both batch learning and existing streaming learning algorithms on
the ImageNet ILSVRC-2012 dataset.

我们提出了流媒体版本的正则判别分析算法，与卷积神经网络结合，证明在 ImageNet ILSVRC-2012 数据集上表现优于批量学习和现有的流媒体学习算法。

深度流式正则判别分析的持续学习

Continual Learning with Deep Streaming Regularized Discriminant Analysis

When an agent acquires new information, ideally it would immediately be
capable of using that information to understand its environment. This is not
possible using conventional deep neural networks, which suffer from
catastrophic forgetting when they are incrementally updated, with new knowledge
overwriting established representations. A variety of approaches have been
developed that attempt to mitigate catastrophic forgetting in the incremental
batch learning scenario, where a model learns from a series of large
collections of labeled samples. However, in this setting, inference is only
possible after a batch has been accumulated, which prohibits many applications.
An alternative paradigm is online learning in a single pass through the
training dataset on a resource constrained budget, which is known as streaming
learning. Streaming learning has been much less studied in the deep learning
community. In streaming learning, an agent learns instances one-by-one and can
be tested at any time, rather than only after learning a large batch. Here, we
revisit streaming linear discriminant analysis, which has been widely used in
the data mining research community. By combining streaming linear discriminant
analysis with deep learning, we are able to outperform both incremental batch
learning and streaming learning algorithms on both ImageNet ILSVRC-2012 and
CORe50, a dataset that involves learning to classify from temporally ordered
samples.

本文研究了基于流式线性判别分析的深度学习方法，以提高流式学习和增量批量学习在分类任务上的性能。其中特别解决了深度神经网络中因为新信息替换旧信息而带来的灾难性遗忘问题，并在两个分类任务上实现了优越的分类效果。

基于深度流线性判别分析的终身机器学习

Lifelong Machine Learning with Deep Streaming Linear Discriminant  Analysis

In supervised machine learning, an agent is typically trained once and then
deployed. While this works well for static settings, robots often operate in
changing environments and must quickly learn new things from data streams. In
this paradigm, known as streaming learning, a learner is trained online, in a
single pass, from a data stream that cannot be assumed to be independent and
identically distributed (iid). Streaming learning will cause conventional deep
neural networks (DNNs) to fail for two reasons: 1) they need multiple passes
through the entire dataset; and 2) non-iid data will cause catastrophic
forgetting. An old fix to both of these issues is rehearsal. To learn a new
example, rehearsal mixes it with previous examples, and then this mixture is
used to update the DNN. Full rehearsal is slow and memory intensive because it
stores all previously observed examples, and its effectiveness for preventing
catastrophic forgetting has not been studied in modern DNNs. Here, we describe
the ExStream algorithm for memory efficient rehearsal and compare it to
alternatives. We find that full rehearsal can eliminate catastrophic forgetting
in a variety of streaming learning settings, with ExStream performing well
using far less memory and computation.

本文介绍了一种名为 ExStream 的算法，通过内存高效的复习来避免深度神经网络在流式学习中出现的灾难性遗忘问题，与传统算法相比表现更好。