论文标题
logits可以预测网络类型
Logits are predictive of network type
论文作者
论文摘要
我们表明，可以预测哪种深网已经生成了一个给定的logit向量，其准确性高于机会。我们在数据集上使用许多网络，并以随机的权重或预处理的重量以及微调的网络初始化。然后，对训练有素的该数据集集合的Logit向量进行了训练，以将logit向量映射到已生成它的网络索引。然后在数据集的测试集上评估分类器。随机初始化的网络的结果更好，但也可以推广到预处理的网络以及微调的网络。使用非均衡ligits比标准化的逻辑更高。我们发现，将分类器应用于同一网络，但具有不同的权重时几乎没有传输。除了帮助更好地了解深层网络及其编码不确定性的方式外，我们预计我们的发现在某些应用程序中有用（例如，针对某种类型的网络量身定制对抗性攻击）。代码可从https://github.com/aliborji/logits获得。
We show that it is possible to predict which deep network has generated a given logit vector with accuracy well above chance. We utilize a number of networks on a dataset, initialized with random weights or pretrained weights, as well as fine-tuned networks. A classifier is then trained on the logit vectors of the trained set of this dataset to map the logit vector to the network index that has generated it. The classifier is then evaluated on the test set of the dataset. Results are better with randomly initialized networks, but also generalize to pretrained networks as well as fine-tuned ones. Classification accuracy is higher using unnormalized logits than normalized ones. We find that there is little transfer when applying a classifier to the same networks but with different sets of weights. In addition to help better understand deep networks and the way they encode uncertainty, we anticipate our finding to be useful in some applications (e.g. tailoring an adversarial attack for a certain type of network). Code is available at https://github.com/aliborji/logits.
