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…
MWCNN — Multi-level Wavelet CNN This is a wavelet-based deep learning architecture. Its architecture has a striking similarity with a U-Net architecture. The only difference in MWCNN is that, unlike down-sampling and up-sampling in U-Net, here we use DWT (Discrete Wavelet Transform) ...
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...
CNNを用いたディープラーニングによる分類の判定精度は非常に高く、多くの領域での画像自動判定に利用されています。一方で、内部がブラックボックスで「なぜその判定になったのかわからない」点に不安を感じる方もいます。Class Activation Mapping(CAM)は判定要因の可視化に非常に便利で...
High-Frequency Component Helps Explain the Generalization of Convolutional Neural Networks 题目:高频成分有助于CNN泛化能力的解释 来源:CVPR 2020 oral 卡内基梅隆大学 1. Motivation CNN的泛化能力不直观,不能直接对其进行解释。 目前对CNN泛化能力进行解释的方法有针对随机梯度... ...
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...
# ...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...
0 링크 번역 Hello, I understand youwant to know on how to usefeature input layer and image input layer. To train a networkcontainingboth an image input layer and a feature input layer, you must use a“dlnetwork”object in a custom training lo...