Machine-learning models have demonstrated great success in learning complex patterns
that enable them to make predictions about unobserved data. In addition to using models for …

Recent research has revealed that the output of deep neural networks (DNNs) can be easily
altered by adding relatively small perturbations to the input vector. In this paper, we analyze …

Feature Visualization Distill About Prize Submit Feature Visualization How neural networks
build up their understanding of images Feature visualization allows us to see how GoogLeNet …

Machine-learning models have demonstrated great success in learning complex patterns
that enable them to make predictions about unobserved data. In addition to using models for …

Deep learning has recently seen rapid development and received significant attention due
to its state-of-the-art performance on previously-thought hard problems. However, because …

Generating high-resolution, photo-realistic images has been a long-standing goal in
machine learning. Recently, Nguyen et al. 2016 showed one interesting way to synthesize …

The tremendous success of ImageNet-trained deep features on a wide range of transfer
tasks begs the question: what are the properties of the ImageNet dataset that are critical for …

Deep neural networks (DNNs) have demonstrated state-of-the-art results on many pattern
recognition tasks, especially vision classification problems. Understanding the inner …

Deep convolutional neural networks (CNNs) have achieved breakthrough performance in
many pattern recognition tasks such as image classification. However, the development of …

We are witnessing a fast development of Artificial Intelligence (AI), but it becomes
dramatically challenging to explain AI models in the past decade.“Explanation” has a flexible …