We greatly appreciate your time and effort in reviewing our paper. We are pleased that you acknowledged the effectiveness of our method across diverse model architectures.
> **Q1: What is the motivation to introduce ISFI?**
We aim to optimize the multiplicative function $\theta(z)$ in Eq. 4, which models elementwise feature-shaping functions. The primary challenge arises from the continuous nature of the feature value $z$, complicating the optimization problem. To address this, we introduce ISFI, a strategic discretization facilitating the assessment of feature importance for OOD detection. Specifically, we partition the domain of features into a disjoint set of intervals, which allows us to approximate the original function $\theta(z)$ with a piece-wise constant function, thus simplifying the optimization problem.
> **Q2: What is the meaning of Figure 2?**
Fig. 2 compares our optimal piecewise constant shaping functions with previous methods, showing that these prior works are empirically approximating our function optimized with access to OOD data.
The methods shown in Fig. 2(a-c), including ReAct, BFAct, and VRA-P, are element-wise shaping functions. These functions identically treat each element of the feature vector, simplifying the comparison.
For example, given any element $z$ of feature representation $\boldsymbol{z}\in \mathbb{R}^M$, our shaping function is $\bar{z}=\theta(z)\cdot z$, where the function $\theta$ is discretized with $K$ parameters. ReAct applies an element-wise shaping function $\bar{z}=\min(z,t)=\frac{\min(z,t)}{z}\cdot z$ to each element. We do not need to compare the two methods on each of the $M$ features, but instead, we compare the two functions $\theta(z)$ and $\frac{\min(z,t)}{z},z\in\mathbb{R}$.
Besides, ASH families in Fig. 2(e-h) incorporate the entire feature vector and are not reducible to element-wise functions. As an approximation, we estimate their average shaping effects across all samples.
> **Q3: Other feature shaping methods such as ReAct and ASH do not have other parameters to be optimized.**
We respectfully argue that this point actually leads to a disadvantage of ReAct and ASH compared with our method. The shaping functions of ReAct and ASH are manually designed for specific model architectures and OOD datasets. This eliminates the need for additional parameters but consequently limit their generalization ability. In contrast, while our shaping function introduces more parameters, it can be efficiently optimized in closed form based on different models and ID data. Additional parameters do not increase user burden; instead, they bring greater flexibility and generalization ability to our method (Tables 1&2).
> **Q4: Can authors discuss the comparable performance claimed by ReAct and ASH?**
There might be some typos in your comment. Here we discuss the generalization ability claimed by ReAct and ASH over different distributions. Please engage with us in discussion if your comment is asking something different.
The experiments conducted in the ReAct and ASH papers involve a narrower range of models and OOD datasets compared to our study. This limitation restricts the evaluation on the generalization ability of their methods.
For instance, when using ImageNet-1k as the ID data, both ReAct and ASH restrict their experiments to two model architectures and four OOD datasets. In contrast, our research encompasses a more extensive evaluation, employing eight different model architectures and eight OOD datasets. Note that the models and OOD datasets used in the ReAct and ASH studies are also included in our experimental setup.
This broader scope in our study allows for a more robust assessment of the generalization ability of our method, significantly contributing to the reliability and applicability of our findings.