Continuous/Lifelong learning of high-dimensional data streams is a
challenging research problem. In fact, fully retraining models each time new
data become available is infeasible, due to computational and storage issues,
while na\"ive incremental strategies have been shown to suffer from
catastrophic forgetting. In the context of real-world object recognition
applications (e.g., robotic vision), where continuous learning is crucial, very
few datasets and benchmarks are available to evaluate and compare emerging
techniques. In this work we propose a new dataset and benchmark CORe50,
specifically designed for continuous object recognition, and introduce baseline
approaches for different continuous learning scenarios.

该论文提出了一种新的数据集和基准 CORe50，并引入了不同连续学习场景的基线方法来解决实际对象识别应用中面临的高维数据流持续学习难题。

CORe50: 一个用于连续物体识别的新数据集及基准

CORe50: a New Dataset and Benchmark for Continuous Object Recognition

Neural machine learning methods, such as deep neural networks (DNN), have
achieved remarkable success in a number of complex data processing tasks. These
methods have arguably had their strongest impact on tasks such as image and
audio processing - data processing domains in which humans have long held clear
advantages over conventional algorithms. In contrast to biological neural
systems, which are capable of learning continuously, deep artificial networks
have a limited ability for incorporating new information in an already trained
network. As a result, methods for continuous learning are potentially highly
impactful in enabling the application of deep networks to dynamic data sets.
Here, inspired by the process of adult neurogenesis in the hippocampus, we
explore the potential for adding new neurons to deep layers of artificial
neural networks in order to facilitate their acquisition of novel information
while preserving previously trained data representations. Our results on the
MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes
lower and upper case letters and digits, demonstrate that neurogenesis is well
suited for addressing the stability-plasticity dilemma that has long challenged
adaptive machine learning algorithms.

本文通过加入新神经元到深度神经网络的层中，探索了连续学习的潜力，并通过 MNIST 手写数字数据集和 NIST SD 19 数据集证明了神经发生有助于解决适应性机器学习算法中的稳定性和可塑性两难问题。

神经元发生的深度学习

Neurogenesis Deep Learning

We apply diffusion strategies to develop a fully-distributed cooperative
reinforcement learning algorithm in which agents in a network communicate only
with their immediate neighbors to improve predictions about their environment.
The algorithm can also be applied to off-policy learning, meaning that the
agents can predict the response to a behavior different from the actual
policies they are following. The proposed distributed strategy is efficient,
with linear complexity in both computation time and memory footprint. We
provide a mean-square-error performance analysis and establish convergence
under constant step-size updates, which endow the network with continuous
learning capabilities. The results show a clear gain from cooperation: when the
individual agents can estimate the solution, cooperation increases stability
and reduces bias and variance of the prediction error; but, more importantly,
the network is able to approach the optimal solution even when none of the
individual agents can (e.g., when the individual behavior policies restrict
each agent to sample a small portion of the state space).

采用扩散策略，将全分布式协作强化学习算法应用于分布式网络，实现仅与直接相邻的智能体通信以改进他们对环境的预测能力，具有线性计算时间和内存占用的高效分布式策略，可应用于离线学习和连续学习，以减少预测误差的偏差和方差，实现全局最优解的学习。