# SAR Target Incremental Recognition Based on Hybrid Loss Function and Class-Bias Correction
## 1 Introduction
(**a**) Non-incremental learning (batch learning) process. (**b**) Incremental learning process. sample preservation, they are combined to alleviate catastrophic forgetting. Firstly, the samples closer to the class center of the old data are selected and preserved, because these samples are more representative and able to better represent a class of targets. Secondly, the cross-entropy loss function and the knowledge distillation loss function of the old and new samples are integrated. The distillation loss between the old and new classes is used to preserve knowledge, and the cross-entropy loss is used to identify different classes. The advantage of this approach is that not only the old representative knowledge but also the knowledge structure are preserved. (2) The old and new target classes are easily confused when similar target classes increase. With the arrival of new targets, the total number of classes to be identified by the model increases, and some visually similar classes may appear in each training phase. When the number of old and new target samples is unbalanced, the increase of visually similar classes will further degrade the recognition performance. On the one hand, this is because the boundary between target classes is more susceptible to class imbalance. On the other hand, it is also because the difference of samples within the same classes and the similarity of samples among different classes are more likely to cause the model to confuse the recognition of old and new classes and produce false recognition results. In [21], a new loss function, i.e., rectification loss, is adopted to deal with the confusion among old and new target classes. However, the disadvantage is that it relies more on the model to select the corresponding samples in the training dataset to calculate this loss. In this paper, a class separation loss function is designed that does not rely on directly selecting samples in the training dataset and can be directly added into the incremental learning. It is capable of making the inter-class sample distance larger, and the intra-class sample distance smaller, promoting the separation of old and new classes. (3) The target recognition model is more likely to classify the input samples as new target classes because there is a weight bias in the fully connected layer of model. The weight bias occurs due to that there is a class imbalance between the old and new classes during training, where the model is able to obtain all samples of the new targets but only a few samples of the old targets. In [12], a balanced training phase is introduced at the end of each model training to fine-tune the model with a balanced dataset. However, these samples cannot fully represent the data distribution and may over-fit the stored samples. In this paper, a bias correction is performed to solve the weight bias problem in the fully connected layer. Firstly, a bias correction layer is added after the fully connected layer of the model, and a linear model with two parameters is used. Secondly, the preserved representative samples are used to train the bias correction layer. This approach enables the model to treat the old and new target classes more fairly and eliminates the bias. In all, the three main contributions of this paper are summarized as follows. * The knowledge distillation method has been introduced into SAR target incremental recognition with the combination of old sample preservation. It is capable of better achieving plasticity-stability balance and preventing catastrophic forgetting of old classes. * An effective loss function for class separation with attention to class boundaries has been designed, which increases inter-class sample distance and decrease intra-class sample distance, therefore facilitates the separation between new and old classes. * A bias correction layer has been designed and trained to solve the problem of weight bias in the fully connected layer, which corrects the biased output of the model. The rest of this paper is organized as follows.