这篇论文来自于CMU的Eric P. Xing团队,探讨了图像的频谱与卷积神经网络(CNN)泛化行为之间的关系。研究表明,CNN 能够捕捉图像中的高频成分(High-frequency Component, HPC),而HPC通常对人类来说几乎不可见…
结论:在通过样本训练出的CNN网络中,一定存在一个样本,使得CNN在任意距离度量方法与鲁棒性阈值下都无法同时满足鲁棒性和准确性均为1的要求,从而导致CNN会在二者之间进行一定的权衡。 为了证明这一结论,本文提出了两个假设: 假设1:在对图像进行观察时,人类只能从低频信息中做出预测,而CNN网络同时会利用高频和低频信息...
# ...include code from https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py import shap import numpy as np # select a set of background examples to take an expectation over background = x_train[np.random.choice(x_train.shape[0], 100, replace=False)] # explain predic...
()# < construct the modelmodel.fit()# < train the modelattributions=de.explain(...)# < compute attributions# Option 2. First create and train your model, then apply DeepExplain.# IMPORTANT: in order to work correctly, the graph to analyze# must always be (re)constructed within the ...
In a recent study [35], researchers used deep learning to build a facial attractiveness assessment model by training a CNN on the SCUT-FBP5500 dataset [34], and they used transfer learning techniques to improve the training accuracy. SCUT-FBP5500 [34] is a new dataset that contains 5500 As...
As always in fastai, a default that works well across a variety of vision datasets is chosen but can be fully customized if needed. learn = cnn_learner(dls, resnet34, metrics=error_rate) Creates a Learner, which combines an optimizer, a model, and the data to train ...
Finally, we will briefly discuss some related topics in Sec- tion 8 before we conclude the paper in Section 9. 2. Related Work The remarkable success of deep learning has attracted a torrent of theoretical work devoted to explaining the gener- ...
In practice, a CNNlearnsthe values of these filters on its own during the training process. (although we still need to specify parameters such asnumbers of filters, filter size, architecture of the network etc. before the training process). ...
By comparison, the basic FCN architecture only had number of classes feature maps in its up-sampling path. U-Net architecture is separated in 3 parts: 1 : The contracting/down-sampling path 2 : Bottleneck 3 : The expanding/up-sampling path Contracting/down-sampling pathThe contracting path ...
# ...include code from https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.pyimportshapimportnumpyasnp# select a set of background examples to take an expectation overbackground=x_train[np.random.choice(x_train.shape[0],100,replace=False)]# explain predictions of the model...