Traditional machine learning systems are deployed under the closed-world
setting, which requires the entire training data before the offline training
process. However, real-world applications often face the incoming new classes,
and a model should incorporate them continually. The learning paradigm is
called Class-Incremental Learning (CIL). We propose a Python toolbox that
implements several key algorithms for class-incremental learning to ease the
burden of researchers in the machine learning community. The toolbox contains
implementations of a number of founding works of CIL such as EWC and iCaRL, but
also provides current state-of-the-art algorithms that can be used for
conducting novel fundamental research. This toolbox, named PyCIL for Python
Class-Incremental Learning, is available at this https URL

该研究提出了一个名为 PyCIL 的 Python 工具箱，实现了几个关键算法，可以用于在面对新类别数据时进行类增量学习。该工具箱包含许多 CIL 的奠基作品的实现，如 EWC 和 iCaRL，同时还提供了一些当前最先进的算法。

PyCIL：用于类增量学习的 Python 工具箱

PyCIL: A Python Toolbox for Class-Incremental Learning

Incremental class learning involves sequentially learning classes in bursts
of examples from the same class. This violates the assumptions that underlie
methods for training standard deep neural networks, and will cause them to
suffer from catastrophic forgetting. Arguably, the best method for incremental
class learning is iCaRL, but it requires storing training examples for each
class, making it challenging to scale. Here, we propose FearNet for incremental
class learning. FearNet is a generative model that does not store previous
examples, making it memory efficient. FearNet uses a brain-inspired dual-memory
system in which new memories are consolidated from a network for recent
memories inspired by the mammalian hippocampal complex to a network for
long-term storage inspired by medial prefrontal cortex. Memory consolidation is
inspired by mechanisms that occur during sleep. FearNet also uses a module
inspired by the basolateral amygdala for determining which memory system to use
for recall. FearNet achieves state-of-the-art performance at incremental class
learning on image (CIFAR-100, CUB-200) and audio classification (AudioSet)
benchmarks.

本论文提出了 FearNet 作为一种新的增量类别学习方法，FearNet 是一种不需要存储先前样本的生成模型，使用神经网络实现一个双重记忆系统，通过处于不同阶段的记忆体以及受哺乳动物海马和额叶前皮层影响的新和长时记忆的网络之间转换以提高记忆效率和防止灾难性遗忘。FearNet 在图像（CIFAR-100，CUB-200）和音频（AudioSet）分类基准测试上均取得了最先进的性能。

FearNet: 增量学习的类脑模型

FearNet: Brain-Inspired Model for Incremental Learning

A major open problem on the road to artificial intelligence is the
development of incrementally learning systems that learn about more and more
concepts over time from a stream of data. In this work, we introduce a new
training strategy, iCaRL, that allows learning in such a class-incremental way:
only the training data for a small number of classes has to be present at the
same time and new classes can be added progressively. iCaRL learns strong
classifiers and a data representation simultaneously. This distinguishes it
from earlier works that were fundamentally limited to fixed data
representations and therefore incompatible with deep learning architectures. We
show by experiments on CIFAR-100 and ImageNet ILSVRC 2012 data that iCaRL can
learn many classes incrementally over a long period of time where other
strategies quickly fail.

本研究提出了一种新的训练策略 iCaRL，可以使得系统能够进行类别的增量式学习，同时学习有效的分类器和数据表示，与其他先前的工作相比，iCaRL 能够持续地增量学习更多的类别，这在 CIFAR-100 和 ImageNet ILSVRC 2012 数据集的实验证明了其可行性。