  Food image classification systems play a crucial role in health monitoring
and diet tracking through image-based dietary assessment techniques. However,
existing food recognition systems rely on static datasets characterized by a
pre-defined fixed number of food classes. This contrasts drastically with the
reality of food consumption, which features constantly changing data.
Therefore, food image classification systems should adapt to and manage data
that continuously evolves. This is where continual learning plays an important
role. A challenge in continual learning is catastrophic forgetting, where ML
models tend to discard old knowledge upon learning new information. While
memory-replay algorithms have shown promise in mitigating this problem by
storing old data as exemplars, they are hampered by the limited capacity of
memory buffers, leading to an imbalance between new and previously learned
data. To address this, our work explores the use of neural image compression to
extend buffer size and enhance data diversity. We introduced the concept of
continuously learning a neural compression model to adaptively improve the
quality of compressed data and optimize the bitrates per pixel (bpp) to store
more exemplars. Our extensive experiments, including evaluations on
food-specific datasets including Food-101 and VFN-74, as well as the general
dataset ImageNet-100, demonstrate improvements in classification accuracy. This
progress is pivotal in advancing more realistic food recognition systems that
are capable of adapting to continually evolving data. Moreover, the principles
and methodologies we've developed hold promise for broader applications,
extending their benefits to other domains of continual machine learning
systems.
