# Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review
## 3 Concept Drift Adaptation Methods under Deep Learning
### Concept Drift Adaptation Methods Based on Hybrid Learning  
Hybrid deep learning models usually consist of multiple deep underlying learning models, either a free combination of discriminative or generative learning or discriminative/generative learning plus other models, such as CNN + LSTM, GAN + CNN, CNN + SVM, and other algorithms, as shown in Table 3.  
The generative model and discriminant model have their own advantages. The generative learning model can learn from unlabeled data and can save labor costs. The discriminant learning model is better than the generative model in supervised tasks. Hybrid deep learning integrates discriminant or generative models according to the target task, and the framework for training deep generative models and discriminant models can enjoy  
\end{table}
Table 2: Concept drift adaptation methods based on generative learning. the advantages of both models to solve real-world problems. The general process of the concept drift adaption method based on generative learning is shown in Figure 7, while the general parameter update mode accounts for a large proportion, and the proportion of active detection and passive adaptation is comparable. Typical algorithms combined with LSTM include HSN-LSTM, online autoregression with deep long short-term memory (OAR-DLSTM), CausalConvLSTM, and LSTMCNNcda. HSN-LSTM [71] is mainly used for multivariate time-series forecasting. It mainly embeds a novel adaptive and hybrid spiking (AHS) module into LSTM to keep the model capable of long-term prediction and alleviate its catastrophic forgetting problem. At the same time, in order to mitigate the impact of concept drift, it adopts the negative log-likelihood function in the fusion attention module to dynamically adjust the attention score and avoid noise interference. However, the resource costs are relatively high. OAR-DLSTM [72] combines a denoising autoencoder, an autoregressive model, and the deep long short-term memory (DLSTM) method, where the denoising encoder is mainly applied to feature extraction, and ORA and DLSTM are applied to target prediction. In the offline state, it divides the training data into data blocks and then pre-trains and retrains the DLSTM model with the error rate predicted by ORA in each data block to obtain several independent sub-models. In the online state, the results of the two models are weighted using a maximum likelihood estimation to obtain the final time-series prediction output. When the dataset is too large, its performance degrades. B-Detection [73] is primarily used to detect runtime reliability anomalies in MEC services. It uses LSTM and AE models to capture the normal reliability data stream distribution characteristics. A weight-based reservoir sampling technique is then used to sample representative normal reliability data. Finally, the sampled data are used for detection model training, and the detection model is retrained to accommodate conceptual drift based on detection performance. However, the run time is relatively long. A typical algorithm for the combination of LSTM and CNN is CausalConvLSTM [74], which utilizes CNN to extract spatial features efficiently and the LSTM model for prediction. It determines whether the model needs to be retrained based on the false-positive rate calculated from the rolling window and updates the network weights to accommodate concept drift by the backpropagation through time (BPTT) algorithm. CausalConvLSTM is primarily used for network intrusion detection but has a problem in that it is limited in the types of logs. Another example is LSTMCNNcda [75] for time-series forecasting, which focuses on actively detecting the occurrence of concept drift and updating the LSTMCNNnet model by an online parameter update when a concept drift is detected but with certain restrictions on the normalized time series and window size selection. In addition, typical algorithms based on hybrid learning include the stacked autoencoder-deep neural network (SAE-DNN), OARIMA-RNN, and recurrent adaptive classifier ensemble (RACE).