We greatly appreciate your further response. We hope the following response can answer your question.
**[To R1].** We respectfully argue that our response does not mention that the reference feature pool is not representative. We think the issue of representativeness you mentioned is not caused by our method, but caused by few-shot normal samples themselves. In our response, we state that ''when the difference between normal images is too large, it **may** cause the reference feature pool is not representative''. With this statement, we want to express that when the normal patterns of one product class are diverse and complex, the few-shot normal samples may lack some normal patterns (*e.g.*, if one class has five colors and one image only contains one color, then 4 reference samples can only contain a maximum of four colors), thus they are not enough to represent the class they belong to. In our method, we only extract the features of the few-shot normal samples and store all the features in the reference feature pool. The reference feature pool does not impair or lose any representation features. For the few-shot normal samples, it is representative enough to them. Therefore, whether the representativeness is sufficient is determined by the few-shot normal samples. For some classes, the few-shot normal samples are representative, while for some hard classes, they may mot be representative enough. For example, in our response to Weakness 3, the results of Macaroni2 and Pipe\_fryum classes are as follows:
| | RDAD | UniAD | SPADE | PaDiM | PatchCore | RegAD | ResAD | WinCLIP | InCTRL | ResAD$^{†}$ |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Macaroni2 | 53.9/91.7 | 56.8/89.4 | 45.2/45.1 | 64.4/87.8 | 61.2/75.8 | 61.9/88.6 | **77.6**/**98.0** | 73.9/89.8 | 74.6/- | **77.6**/96.9 |
| Pipe\_fryum | 70.2/92.0 | 46.7/91.4 | 72.0/90.4 | 88.7/99.2 | 85.9/97.2 | 92.1/98.9 | 98.9/**98.5** | 92.0/96.5 | 86.9/- | **99.3**/**98.5** |
For the Pipe\_fryum class, the few-shot reference samples are representative enough. For the Macaroni2 class, the few-shot reference samples are not representative enough, and this issue exists for all AD methods that use few-shot normal samples, as it is limited by the data rather than the method itself. Thus, this issue should be addressed by the data perspective, in practical applications, when few-shot normal samples are insufficient to represent their class, we can increase the number of reference samples (or use the method we stated in the response to Reviewer 226V's Weakness 3). However, for method comparison, the few-shot setting is feasible (InCTRL also follows this setting). Because we ensure that all methods use the same reference samples, all methods can obtain the same representation information, the result comparison is reasonable and fair.
Matching the nearest reference features is to generate residual features, InCTRL also employs this way to generate residuals (please see our response to Reviewer fL7E's Weakness 1), which also indicates that this way is reasonable and effective for residual generation. We think that besides residual features, the other parts of our method also play a crucial role, *e.g.* the Feature Constraintor and the Abnormal Invariant OCC loss (please see Table 2(a) in our paper). In addition, we note that in SPADE, each test feature will search for the nearest normal feature and directly calculate the distance between the two features as the anomaly score. By comparison, our method can significantly outperform SPADE, this also indicates that residual feature learning (v.s. only searching for the nearest normal features) is more effective for utilizing few-shot normal samples.