  Recent years have witnessed enormous progress of online learning. However, a
major challenge on the road to artificial agents is concept drift, that is, the
data probability distribution would change where the data instance arrives
sequentially in a stream fashion, which would lead to catastrophic forgetting
and degrade the performance of the model. In this paper, we proposed a new
Bilevel Online Deep Learning (BODL) framework, which combine bilevel
optimization strategy and online ensemble classifier. In BODL algorithm, we use
an ensemble classifier, which use the output of different hidden layers in deep
neural network to build multiple base classifiers, the important weights of the
base classifiers are updated according to exponential gradient descent method
in an online manner. Besides, we apply the similar constraint to overcome the
convergence problem of online ensemble framework. Then an effective concept
drift detection mechanism utilizing the error rate of classifier is designed to
monitor the change of the data probability distribution. When the concept drift
is detected, our BODL algorithm can adaptively update the model parameters via
bilevel optimization and then circumvent the large drift and encourage positive
transfer. Finally, the extensive experiments and ablation studies are conducted
on various datasets and the competitive numerical results illustrate that our
BODL algorithm is a promising approach.
