  Modern deep learning approaches have achieved great success in many vision
applications by training a model using all available task-specific data.
However, there are two major obstacles making it challenging to implement for
real life applications: (1) Learning new classes makes the trained model
quickly forget old classes knowledge, which is referred to as catastrophic
forgetting. (2) As new observations of old classes come sequentially over time,
the distribution may change in unforeseen way, making the performance degrade
dramatically on future data, which is referred to as concept drift. Current
state-of-the-art incremental learning methods require a long time to train the
model whenever new classes are added and none of them takes into consideration
the new observations of old classes. In this paper, we propose an incremental
learning framework that can work in the challenging online learning scenario
and handle both new classes data and new observations of old classes. We
address problem (1) in online mode by introducing a modified cross-distillation
loss together with a two-step learning technique. Our method outperforms the
results obtained from current state-of-the-art offline incremental learning
methods on the CIFAR-100 and ImageNet-1000 (ILSVRC 2012) datasets under the
same experiment protocol but in online scenario. We also provide a simple yet
effective method to mitigate problem (2) by updating exemplar set using the
feature of each new observation of old classes and demonstrate a real life
application of online food image classification based on our complete framework
using the Food-101 dataset.
