In this paper, we have presented a method of combining the weight optimizations from ternary weight networks with a set of custom features inspired by the Viola–Jones Object Detection Framework to improve a network's performance. By replacing the first convolutional layer in a network with a function using a custom set of filters, we can reduce the number of operations required to compute the layer's outputs. These filters are composed of four rectangles, labeled as either positive or negative, within the filter space. When combined with an integral image, these filters allow for more efficient computations of feature values than a standard convolutional layer. When using our custom filters of shape 5 × 5, the layer only requires 64% of the operations that a standard convolutional layer need. This reduction only increases as the filter sizes get larger. Empirically, we have found that these layers' real-world execution times behave as theoretically calculated and that their optimizations do not lead to significant drop inaccuracy. During testing, we evaluated our proposed optimization on two different data sets with two different network architectures.

中文翻译：

---

使用 Viola-Jones 框架和三元权重网络优化卷积网络层

在本文中，我们提出了一种将三元权重网络的权重优化与一组受 Viola–Jones 对象检测框架启发的自定义特征相结合的方法，以提高网络性能。通过使用使用一组自定义过滤器的函数替换网络中的第一个卷积层，我们可以减少计算该层输出所需的操作数量。这些过滤器由过滤器空间内的四个矩形组成，标记为正或负。当与积分图像结合使用时，这些滤波器允许比标准卷积层更有效地计算特征值。当使用我们的 5 × 5 形状的自定义过滤器时，该层只需要标准卷积层所需操作的 64%。这种减少只会随着过滤器尺寸变大而增加。根据经验，我们发现这些层的实际执行时间与理论上计算的一样，并且它们的优化不会导致显着的不准确度下降。在测试期间，我们在具有两种不同网络架构的两个不同数据集上评估了我们提出的优化方案。