前向神经网络特征可视化过程的反演
Inverting the Feature Visualization Process for Feedforward Neural Networks
摘要：
这项工作揭示了神经网络中特征可视化的可逆性。由于使用激活最大化的特征可视化生成的输入一般不会产生它被优化的特征目标，因此我们研究优化产生此输入的特征目标。给定用于激活最大化的目标函数，该目标函数测量给定输入与特征目标的相似程度，我们利用该函数的梯度w.r.t.输入在目标中是线性的。该观察结果用于通过计算最小化梯度的封闭形式解决方案来找到最佳特征目标。通过逆向特征可视化，我们打算提供一个替代的观点，对网络的敏感性，考虑某些输入的功能目标，而不是激活。
英文摘要：
This work sheds light on the invertibility of feature visualization in neural networks. Since the input that is generated by feature visualization using activation maximization does, in general, not yield the feature objective it was optimized for, we investigate optimizing for the feature objective that yields this input. Given the objective function used in activation maximization that measures how closely a given input resembles the feature objective, we exploit that the gradient of this function w.r.t. inputs is---up to a scaling factor---linear in the objective. This observation is used to find the optimal feature objective via computing a closed form solution that minimizes the gradient. By means of Inverse Feature Visualization, we intend to provide an alternative view on a networks sensitivity to certain inputs that considers feature objectives rather than activations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
