# SAR Target Incremental Recognition Based on Hybrid Loss Function and Class-Bias Correction
## 2 Relative Work
### Old Sample Preservation  
Multi-class incremental learning without forgetting (mnemonics training) [33] is a method based on old sample preservation, and it is similar to [8; 12; 28; 29]. The method optimizes the framework in both model-level and sample-level to ensure that the reserved samples are capable of well representing the boundary and mean of each class's distribution. However, it relies on well-preprocessed feature extractors to obtain superior performance of incremental learning. The Class Boundary exemplar selection based Incremental Learning (CBesIL) for automatic target recognition [6] mainly consists of two parts, i.e., class boundary selection and incremental learning. The class boundary selection is based on  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Methods** & **Year** & \begin{tabular}{l} **Old Data** \\ **Preservation** \\ \end{tabular} & \begin{tabular}{l} **Knowledge** \\ **Distillation** \\ \end{tabular} & \
\begin{tabular}{l} **Class** \\ **Balancing** \\ \end{tabular} &
\begin{tabular}{l} **Application Field** \\ **(Dataset Name)** \\ \end{tabular} \\ \hline LWF [15] & 2016 & & & ✓ & & Places365-standard, ILSVRC 2012 \\ iCaRL [8] & 2017 & ✓ & ✓ & ✓ & CIFAR-100, \\  & & & & & ILSVRC 2012. \\ EWC [9] & 2017 & & & & MNIST. \\ ICL-GAN [26] & 2018 & & ✓ & & CIFAR-100, Flower-102, MS-Celeb-1M-Base. \\ EEIL [12] & 2018 & ✓ & ✓ & ✓ & CIFAR-100, \\  & & & & & ILSVRC 2012. \\ LWM [27] & 2019 & & ✓ & & iCIFAR-100, \\  & & & & & iiLSVRC-small. \\ United [28] & 2019 & ✓ & ✓ & ✓ & CIFAR-100, \\  & & & & & ImageNet-Subset. \\ Bic [29] & 2019 & ✓ & ✓ & ✓ & ImageNet-1000, \\  & & & & & MS-Celeb1M. \\ M2 KD [30] & 2019 & & ✓ & & CIFAR-100, \\  & & & & & iILSVRC-small. \\ IL2M [31] & 2019 & ✓ & ✓ & ✓ & ILSVRC, VGGFace2, \\  & & & & & Landmarks. \\ ScaIL [32] & 2020 & ✓ & & & ILSVRC, VGGFace2, \\  & & & & & Landmarks, CIFAR-100. \\ Mnemonics Training [33] & 2020 & ✓ & ✓ & & CIFAR-100, ILSVRC 2012. \\ CBesIL [6] & 2020 & ✓ & & & MSTAR. \\ Our-method & 2021 & ✓ & ✓ & ✓ & MSTAR. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of existing incremental learning methods.  
local geometric information and statistical information to extract class boundaries. It performs distribution reconstruction to update the sample set when new classes are added. However, it only considers protecting class boundary updates during data reconstruction, which will cause inaccuracy of recognition. The overcoming of catastrophic forgetting in neural networks (EWC) [9] evaluates the importance of network synapses by computing the Fisher information matrix, which is used to slow down the learning of weights that highly correlate with the previous task. However, this approach also leads to an imbalance for new tasks. The classifier weights Scaling for class Incremental Learning (ScaIL) [32] reuses the network weights learned from all data to reduce bias. However, it depends on comparing classifier weights for the current state and the initial state.  
It is also a feasible way to generate pseudo-samples of old data using Generative Adversarial Networks (GAN), reducing storage consumption. [PERSON] et al. [26] use GAN to generate pseudo images for old classes and combine these images with new class's images and achieve slightly better performance than [8]. However, the quality of the pseudo-samples is not able to entirely represent the actual samples. Therefore the performance will significantly degrade if only relying on the generated pseudo-samples. Moreover, it is not the optimal incremental approach due to its requirement of creating more GAN models and more memory cost.  
Based on the above analysis, this paper adopts the old sample preservation strategy so that the model does not forget what it has learned. When a new target arrives, the first \(k\) samples closer to the class center of each target are preserved.