A Mask R-CNN model was trained with synthetic CEM images containing microcalcification clusters. After a training run of 100 epochs the model was found to overfit on a training set of 192 images. In an evaluation with multiple test sets, it was found that this high level of sensitivity was...
As a result, OMENN provides locally precise, attribution-based explanations of the input across various modern models, including ViTs and CNNs. We present a theoretical analysis of OMENN based on dynamic linearity property and validate its effectiveness with extensive tests on two XAI benchmarks, ...
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…
High-frequency Component Helps Explain the Generalization of Convolutional Neural Networks CNN泛华论文解读 通常情况下,CNN表现特征和肉眼可理解的特征存在一定的出入,在CNN泛化能力上难以理解。本文以CMU 的汪浩瀚、邢波等人《High-frequency Component Helps Explain...数据集 label 的相互关联。当模型优化去降低损失...
在只使用低频成分进行目标检测时,模型在下图的上半部分获得了更低的MAP值,而在下半部分的图像中获得了更好的MAP结果,而对于人类而言没有明显区别,显示出CNN与人类思维模式的仍有差异。编辑于 2021-01-19 21:57 深度学习(Deep Learning) 卷积神经网络(CNN) 人工智能 赞同191 条评论 分享喜欢...
Related machine learning frameworks In terms of purpose, ExplAIn is related to existing algorithms for visualizing/interpreting what image classification CNNs have learnt. Given a trained classification CNN and an input image, these algorithms compute the influence of each pixel on CNN predictions. In...
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- ...
deepexplain Fixed check of input shapes when dealing with multiple inputs. Sep 26, 2019 docs docs image Nov 8, 2017 examples Adapt MNIST CNN example Mar 15, 2019 .gitignore ignore data files Feb 20, 2019 .travis.yml minor Mar 17, 2019 LICENSE.md Updated LICENSE, READE and dependencies ...
# ...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...
High-frequency Component Helps Explain the Generalization of Convolutional Neural Networks CNN泛华论文解读,程序员大本营,技术文章内容聚合第一站。