# 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 Generative Learning  
Generative learning technologies are often used to describe higher-order correlation attributes or features for pattern analysis or synthesis, as well as joint statistical distributions of visible data and their related classes [55]. Most generative learning is unsupervised learning, but sometimes it can also be used for preprocessing in supervised learning, dimensionality reduction processing, etc. [56]. A generative model learns the data generation process, learns the probability distribution of input data, and generates new samples of data. More specifically, the generative model first estimates the conditional density of the classes \(P(a\,|\,b=k)\) and the prior class probability \(P(b=k)\) from the training data. They tried to understand how the data for each classification was generated. [PERSON] theorem is then used to estimate the posterior class probability. Generative models can also learn the joint distribution of inputs and labels \(P(a,b)\) and then normalize them to obtain posterior probabilities \(P(b=k\,|\,a)\). The general process of the conceptual drift adaptive method based on generative learning is shown in Figure 6, while the general parameter update mode accounts for a large proportion, and the proportion of active detection and passive adaptation is comparable. Common deep neural network technologies for unsupervised or generative learning are generative adversarial networks (GANs), autoencoders (AEs), restricted Boltzmann machines (RBMs), self-organizing mapping (SOM), and deep belief networks (DBNs) and their variants. * AE-based concept drift adaptation methods  
AE mainly consists of an encoder, a code, and a decoder [57]. It is combined with a concept drift adaptation method, which is mainly used for the anomaly detection of some high-dimensional data, such as the detection of the anomalous behavior of elderly people. Typical algorithms include the adaptive framework for online deep anomaly detection (ARCUS), unsupervised statistical concept drift detection (USCDD-AE), deep evolving denoising autoencoder (DEVDAN), and memory-based streaming anomaly detection (MemStream). ARCUS [58] contains concept-driven inference and drift-aware model pool updates, where concept-driven inference focuses on evaluating the reliability of its models and giving evaluation scores when given a new data point. When a concept drift occurs, its evaluation score will drop to trigger a model pool update. Some models will then be removed and retrained to adapt to the occurrence of concept drift. In this process, the algorithm mainly uses the same structure of the AE model to form a model pool to perform anomaly detection of the data flow, which mainly has a large resource cost and cannot store the current batch of data where concept drift may occur. USCDD-AE [59] uses variational autoencoders to identify the anomalies of elderly people, which detects concept drift based  
Figure 6: The general process of concept drift adaptation methods based on generative learning. on data from families and the activity probability plot of the Kullback-Leibler divergence, as defined below. \[D_{KL}(G\parallel Q)=\sum_{z\in Z}G(z)\log(\frac{G(z)}{Q(z)}) \tag{6}\]  
where \(Z\) is the probability space, and \(G\) and \(Q\) are probability distributions defined over \(Z\). Here \(G\) and \(Q\) are activity probability maps. Then, when concept drift occurs, the encoder will be updated to adapt to concept drift by backpropagating the reconstruction error. In this process, there are often difficulties with data collection and the possibility of false positives. DEVDAN [60] is an incremental learning method that primarily uses the network significance formula to evaluate the predictive power of the model. Once the value in the capture formula rises, its hidden nodes are adjusted. USCDD-AE and DEVDAN are mainly based on the active concept drift adaptation method but ignore mutation oblivion when adding new layers.