# Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review
## 3 Concept Drift Adaptation Methods under Deep Learning
### Other Concept Drift Adaptation Methods
ATL [84] uses the autonomous Gaussian mixture model (AGMM) to automatically adjust the network width, which solves the concept drift problem. It is just a matter of readapting to a concept that has been there before when it reappears. An adaptive anomaly detection approach toward concept drift (ADTCD) [85] is an adaptive anomaly detection model based on knowledge distillation and DTL. It transfers knowledge from the AE-based teacher model to the student model and updates only the student model, which dynamically adjusts model weights to accommodate concept drift primarily through local inference on new samples. However, the algorithm also suffers from two limitations. Firstly, the industrial scenarios used for the experiments are relatively homogeneous, and secondly, little attention is paid to scarce anomaly data. * DRL-based concept drift adaptation methods  
DRL combines the perception ability of deep learning with the decision-making ability of reinforcement learning, which can be directly controlled based on the input information. Reinforcement learning defines the goal of optimization, and deep learning gives the mechanism by which it works (how to characterize problems and how to solve them) [86]. The algorithms using this concept drift adapting method are mainly applied in the fields of financial investment and anomaly detection. Typical algorithms include DeepPocket, RL4 OAASD, online ensemble aggregation using reinforcement learning (OEA-RL), and DeepBreath. DeepPocket [87] is used in the field of financial investment. This algorithm mainly uses a restricted stacked autoencoder to extract features and uses two convolutional networks  
Figure 8: The general process of concept drift adaptation methods based on other deep learning. to find the best portfolio through deep reinforcement learning. Then, it uses online training to dynamically update weights to accommodate concept drift, but it does not lend itself to a long-term investment strategy. RL4 OASD [88] is mainly used for the detection of abnormal trajectories of vehicles. It includes two networks: one is responsible for learning the features of the road network and trajectory, and the other is responsible for detecting anomalous sub-traces based on the learned features. The two networks can be trained iteratively without labeled data, and they employ an online learning strategy; that is, they are trained with newly recorded trajectory data and continuously update their strategies based on current traffic conditions, but they have a longer training time. OEA-RL [89] mainly uses the deep reinforcement learning framework as a meta-learning method to learn linear weighted ensembles and actively detects the occurrence of concept drift through the Page-Hinkley (PH) test. Then, it adapts to concept drift by updating its parameters. Again, there is a certain delay in updating due to its active detection algorithm. DeepBreath [6] is mainly used for financial investment, which uses a limited superimposed autoencoder for dimensionality reduction and feature processing. Then, the SARSA algorithm and the online batch processing method are used to train CNN learning investment strategies from historical data, and after training the model, the weights are updated through the online learning scheme to adapt to the concept drift. The algorithm lacks, to some extent, the consideration of exogenous factors. As can be seen in Table 4, for DRL and DTL, the update mode of DRL is mainly a parameter update. It interacts well with the environment to learn the sequence of its behavior. The update mode of DTL is mainly a structural update. DTL can effectively use a small amount of data to train neural networks. This characteristic can use structure updates to train better predictive models. In addition, they generally use a combination of online and offline approaches to adapt concept drift and support more complex predictions. In addition to DTL and DRL, two more popular deep learning methods, there are also concept drift adaption methods based on other deep learning technologies.