Image compression emerges as a pivotal tool in the efficient handling and
transmission of digital images. Its ability to substantially reduce file size
not only facilitates enhanced data storage capacity but also potentially brings
advantages to the development of continual machine learning (ML) systems, which
learn new knowledge incrementally from sequential data. Continual ML systems
often rely on storing representative samples, also known as exemplars, within a
limited memory constraint to maintain the performance on previously learned
data. These methods are known as memory replay-based algorithms and have proven
effective at mitigating the detrimental effects of catastrophic forgetting.
Nonetheless, the limited memory buffer size often falls short of adequately
representing the entire data distribution. In this paper, we explore the use of
image compression as a strategy to enhance the buffer's capacity, thereby
increasing exemplar diversity. However, directly using compressed exemplars
introduces domain shift during continual ML, marked by a discrepancy between
compressed training data and uncompressed testing data. Additionally, it is
essential to determine the appropriate compression algorithm and select the
most effective rate for continual ML systems to balance the trade-off between
exemplar quality and quantity. To this end, we introduce a new framework to
incorporate image compression for continual ML including a pre-processing data
compression step and an efficient compression rate/algorithm selection method.
We conduct extensive experiments on CIFAR-100 and ImageNet datasets and show
that our method significantly improves image classification accuracy in
continual ML settings.

本论文提出了一种新的框架，通过图像压缩来增强连续机器学习中缓冲区的容量，包括预处理数据压缩步骤和高效的压缩率 / 算法选择方法，通过在 CIFAR-100 和 ImageNet 数据集上进行广泛实验，证明了该方法在连续机器学习环境中显著提高了图像分类的准确性。

探究图像压缩对于类别增量学习的影响

Probing Image Compression For Class-Incremental Learning

Artificial neural networks have exceeded human-level performance in
accomplishing several individual tasks (e.g. voice recognition, object
recognition, and video games). However, such success remains modest compared to
human intelligence that can learn and perform an unlimited number of tasks.
Humans' ability of learning and accumulating knowledge over their lifetime is
an essential aspect of their intelligence. Continual machine learning aims at a
higher level of machine intelligence through providing the artificial agents
with the ability to learn online from a non-stationary and never-ending stream
of data. A key component of such a never-ending learning process is to overcome
the catastrophic forgetting of previously seen data, a problem that neural
networks are well known to suffer from. The work described in this thesis has
been dedicated to the investigation of continual learning and solutions to
mitigate the forgetting phenomena in neural networks. To approach the continual
learning problem, we first assume a task incremental setting where tasks are
received one at a time and data from previous tasks are not stored. Since the
task incremental setting can't be assumed in all continual learning scenarios,
we also study the more general online continual setting. We consider an
infinite stream of data drawn from a non-stationary distribution with a
supervisory or self-supervisory training signal. The proposed methods in this
thesis have tackled important aspects of continual learning. They were
evaluated on different benchmarks and over various learning sequences. Advances
in the state of the art of continual learning have been shown and challenges
for bringing continual learning into application were critically identified.

通过研究任务增量设置和在线连续设置，本文提出了一些方法以解决神经网络的严重遗忘问题，并在不同的基准测试和学习序列上进行了评估，显示了连续学习技术现在的发展和应用面临的挑战。