Add to ORKGNeural Networks (NNs) have become a basis of almost all state-of-the-art machine learning algorithms and classifiers. While NNs have been shown to generalize well to real-world examples, researchers have struggled to show why they work on an intuitive level. We designed several methods to explain the decisions of two state-of-the-art NN classifiers, ResNet and an All-CNN, in the context of the Japanese Society of Radiological Technology (JSRT) lung nodule dataset and the CIFAR-10 image dataset. Leading explanation methods LIME and Grad-CAM generate variations of heat maps which represent the regions of the input determined salient by the NN. We analyze these salient regions highlighted by these algorithms, show how these explanations may be...
One way to interpret trained deep neural networks (DNNs) is by inspecting characteristics that neuro...
To identify the best transfer learning approach for the identification of the most frequent abnormal...
To identify the best transfer learning approach for the identification of the most frequent abnormal...
Neural Networks (NNs) are increasingly used as the basis of advanced machine learning techniques in ...
This dissertation concerns methods for improving the reliability and quality of explanations for dec...
As deep learning techniques have become more prevalent in computer vision, the need to explain these...
As deep learning techniques have become more prevalent in computer vision, the need to explain these...
As deep learning techniques have become more prevalent in computer vision, the need to explain these...
Deep Learning has attained state-of-the-art performance in the recent years, but it is still hard to...
Lack of explainability in artificial intelligence, specifically deep neural networks, remains ...
Deep convolutional neural networks have proven their effectiveness, and have been acknowledged as th...
Deep neural networks (DNNs) have greatly impacted numerous fields over the past decade. Yet despite ...
Deep learning models have been increasingly applied to medical images for tasks such as lesion detec...
We investigate the influence of adversarial training on the interpretability of convolutional neural...
Neural networks have frequently been found to give accurate solutions to hard classification problem...
One way to interpret trained deep neural networks (DNNs) is by inspecting characteristics that neuro...
To identify the best transfer learning approach for the identification of the most frequent abnormal...
To identify the best transfer learning approach for the identification of the most frequent abnormal...
Neural Networks (NNs) are increasingly used as the basis of advanced machine learning techniques in ...
This dissertation concerns methods for improving the reliability and quality of explanations for dec...
As deep learning techniques have become more prevalent in computer vision, the need to explain these...
As deep learning techniques have become more prevalent in computer vision, the need to explain these...
As deep learning techniques have become more prevalent in computer vision, the need to explain these...
Deep Learning has attained state-of-the-art performance in the recent years, but it is still hard to...
Lack of explainability in artificial intelligence, specifically deep neural networks, remains ...
Deep convolutional neural networks have proven their effectiveness, and have been acknowledged as th...
Deep neural networks (DNNs) have greatly impacted numerous fields over the past decade. Yet despite ...
Deep learning models have been increasingly applied to medical images for tasks such as lesion detec...
We investigate the influence of adversarial training on the interpretability of convolutional neural...
Neural networks have frequently been found to give accurate solutions to hard classification problem...
One way to interpret trained deep neural networks (DNNs) is by inspecting characteristics that neuro...
To identify the best transfer learning approach for the identification of the most frequent abnormal...
To identify the best transfer learning approach for the identification of the most frequent abnormal...