We then investigate the cause and show how such a trade-off can be exploited for either good or bad purposes.

This problem is known to be complicated by many unobserved confounders.

There has been an enormous interest in deploying deep neural networks (DNNs) into numerous real-world applications requiring strict security.

In recent years, neural implicit representations gained popularity in 3D reconstruction due to their expressiveness and flexibility. However, the implicit nature of neural implicit representations results in slow inference time and requires careful initialization.

Video-quality measurement is a critical task in video processing.

Intuitively, ASH activation function is the threshold-based activation function rectifying inputs, and we obtained the following properties: Property 1. ASH activation function is parametric. ASH activation function in the early layer exhibits a small threshold (large percentile) to retain substantial information, whereas ASH in deeper layers exhibits a small comparative percentile to rectify futile information. Property 2. ASH activation function provides output concerning the contexts of the input. Supplementary Figure 1 illustrates the training graph of loss values and validation accuracies. In addition, the y-axis indicates the range of (0, 0.8). 3 Appendix D. Classification task Supplementary Figure 1 illustrates the GRAD-CAM (Selvaraju et al., 2017) samples by using ResNet-164 and Dense-Net models with ReLU, Swish, and ASH activation function in the classification task In Supplementary Figure 1 Property 1 is clearly illustrated.