Lifelong machine learning (LML) is an area of machine learning research
concerned with human-like persistent and cumulative nature of learning. LML
system's objective is consolidating new information into an existing machine
learning model without catastrophically disrupting the prior information. Our
research addresses this LML retention problem for creating a knowledge
consolidation network through task rehearsal without retaining the prior task's
training examples. We discovered that the training data reconstruction error
from a trained Restricted Boltzmann Machine can be successfully used to
generate accurate virtual examples from the reconstructed set of a uniform
random set of examples given to the trained model. We also defined a measure
for comparing the probability distributions of two datasets given to a trained
network model based on their reconstruction mean square errors.

本研究旨在解决终身机器学习模型在接收新信息而不影响先前学习经验的情况下的知识整合问题，在不使用先前任务的训练示例的情况下，通过任务练习方式创建一个知识整合网络，同时使用训练后的 Restricted Boltzmann Machine 所产生的训练数据重建误差成功生成准确的虚拟示例，并定义了一种基于重建均方误差比较两个数据集的概率分布的度量方法。

为终身机器学习生成准确的虚拟实例

Generating Accurate Virtual Examples For Lifelong Machine Learning

We compare the robustness of humans and current convolutional deep neural
networks (DNNs) on object recognition under twelve different types of image
degradations. First, using three well known DNNs (ResNet-152, VGG-19,
GoogLeNet) we find the human visual system to be more robust to nearly all of
the tested image manipulations, and we observe progressively diverging
classification error-patterns between humans and DNNs when the signal gets
weaker. Secondly, we show that DNNs trained directly on distorted images
consistently surpass human performance on the exact distortion types they were
trained on, yet they display extremely poor generalisation abilities when
tested on other distortion types. For example, training on salt-and-pepper
noise does not imply robustness on uniform white noise and vice versa. Thus,
changes in the noise distribution between training and testing constitutes a
crucial challenge to deep learning vision systems that can be systematically
addressed in a lifelong machine learning approach. Our new dataset consisting
of 83K carefully measured human psychophysical trials provide a useful
reference for lifelong robustness against image degradations set by the human
visual system.

通过对三种著名的深度卷积神经网络在十二种不同的图像失真下的实验对比，研究表明，与这三种人工智能算法相比，人类视觉系统在几乎所有的测试图像处理中都更加鲁棒，其误差模式渐行渐远。本文还证明了，利用失真图像直接训练的深度学习神经网络在恰当的测试条件下，表现优于人类。然而，当被分别用于测试不同类型的失真图像时，它们的泛化能力极差，无法适应噪声分布的变化，这成为深度学习视觉系统所面临的关键挑战，可用一种终身机器学习的方法进行系统化解决。

人类与深度神经网络的泛化能力

Generalisation in humans and deep neural networks

Lifelong machine learning methods acquire knowledge over a series of
consecutive tasks, continually building upon their experience. Current lifelong
learning algorithms rely upon a single learning agent that has centralized
access to all data. In this paper, we extend the idea of lifelong learning from
a single agent to a network of multiple agents that collectively learn a series
of tasks. Each agent faces some (potentially unique) set of tasks; the key idea
is that knowledge learned from these tasks may benefit other agents trying to
learn different (but related) tasks. Our Collective Lifelong Learning Algorithm
(CoLLA) provides an efficient way for a network of agents to share their
learned knowledge in a distributed and decentralized manner, while preserving
the privacy of the locally observed data. Note that a decentralized scheme is a
subclass of distributed algorithms where a central server does not exist and in
addition to data, computations are also distributed among the agents. We
provide theoretical guarantees for robust performance of the algorithm and
empirically demonstrate that CoLLA outperforms existing approaches for
distributed multi-task learning on a variety of data sets.

本文提出了一种利用多智能体网络进行分布式的集体终身学习算法（CoLLA），通过分布式地共享学习知识，在维护本地数据隐私的同时，在多个任务上表现出更好的性能，同时提供了理论保障。