# Predictive Distillation Method of Anchor-Free Object Detection Model for Continual Learning
## 1 Introduction  
Compared to traditional computer vision algorithms, deep learning shows innovative performance, and it is accordingly being applied in a variety of fields. In areas where deep learning is successfully applied, the label of the data is completely defined before the deep learning model is learned, and the input, output, and internal layer structure of the model is fixed. In [PERSON] and [PERSON]'s 'Life Machine Learning' [1], this environment is called isolated learning, and the application of continual learning is stated to be essential for deep learning to develop in a manner similar to human intelligence. Human learning is a type of cumulative learning wherein what was previously learned in the learning process is not forgotten. Assuming that the classification target is changed several times to re-learn the pre-learned deep learning model, most of the learned knowledge is forgotten and changed to suit the new target in the general learning paradigm process. Because the existing learning paradigm cannot accumulate what has been learned before, if re-learning is performed several times by changing the target, then the model will no longer be able to classify objects that have previously been well-classified. Continual learning (CL) is proposed to solve this problem and can be distinguished from other learning paradigms in that it accumulates previously learned knowledge so that it can be applied to constantly increasing classification targets [1; 2; 3; 4]. The CL paradigm must simultaneously classify previous and current objects well while preventing the problem of forgetting previously learned knowledge. In the re-learningprocess, the problem of not being able to classify objects that were previously well-classified is called catastrophic forgetting. The main purpose of CL is to derive the minimum oblivion for all previously learned tasks and the maximum inference accuracy for new tasks [1,2]. In this study, we propose an object detection CL model that applies the knowledge distillation (KD) [5,6] method to minimize catastrophic forgetting. The object detection algorithm performs a task in which the name, location, and size of an object in an image are predicted simultaneously. Most object detection models in deep learning use anchor boxes to detect the size and location of objects. However, the problem with the anchor box is that it leads to more parameters, and therefore, a higher computational cost. Moreover, in a real-world environment, the object, size, and characteristics of continuously increasing data are not known in advance, so the anchor box is not suitable for CL. Therefore, the object detection model used in the proposed model is CenterNet [7], which is an anchor-free model that ensures a certain level of accuracy, even with a small number of parameters [8]. In KD, one of the types of algorithms for CL, the previously learned model is referred to as the teacher model (t-model), whereas the model that classifies the newly learned object is referred to as the student model (s-model). In general, object detection CL models using KD mainly employ the method of distilling a specific layer of the t-model in the s-model to minimize the differences between the two models [5,6]. Since CL research involves a very large range of scenarios and algorithms, the model proposed in this study has the following limitations and conditions. * It targets class incremental learning (class IL) without a separate task ID. * A scenario is set up assuming that data and labels cannot be reused. * It targets CL models that do not store samples or feature maps of training data so that learning data cannot be estimated. * The model structure is fixed to avoid increasing the parameters such that the inference time of the model does not increase. * In the next training step, information on the previous class object can only be delivered using the weights of the previous learning model. * Scenarios are pre-defined according to the order of CL learning.