**ImageNet-C** is an open source data set that consists of algorithmically generated corruptions (blur, noise) applied to the ImageNet test-set.
What is neural network robustness?
Intuitively, local robustness [7]–[9] is defined for a given input x and states that the neural network should produce the same result (e.g., label) for x and for all inputs x within a ball of radius δ centered at x. (Notice that this definition relies on a suitable distance metric defined over the input space.).
What is probabilistic robustness quantification of neural networks?
Probabilistic robustness guarantees that a neural network is robust with at least (1 − ϵ) probability, given a real-world input probability distribution. In contrast to existing notions of robustness, probabilistic robustness focuses on a non- adversarial setting..
What is robustness in NN?
Robustness is defined as the ability of a neural network to map untrained data (data not used during training) within an error tolerance. An induced Euclidean matrix norm is used to derive error bounds for NN with activation functions that predominantly exhibit linear behavior..
Unlike recent robustness research, this benchmark evaluates performance on common corruptions and perturbations not worst-case adversarialÂ
In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, IMAGENET-C, standardizes and expands the corruption.
How do we benchmark surface variation robustness?
To benchmark surface variation robustness, we introduce the ICONS-50 dataset. This dataset is intended to support research on robustness to surface variations such as:
the introduction of new styles and novel animal species. We then describe two methods that improve surface variation robustness.
What is a multiscale neural network?
Multiscale, Feature Aggregating, and Larger Networks. Multiscale architectures achieve greater robustness by propagating features across scales at each layer rather than slowly gaining a global representation of the input as in typical convolutional neural networks. Some multiscale architectures are called Multigrid Networks .
What is the corruption robustness benchmark?
Our corruption robustness benchmark consists of 15 diverse corruption types, exemplified in Figure 1. The benchmark covers noise, blur, weather, and digital categories. Research that improves performance on this benchmark should indicate general robustness gains, as the corruptions are varied and great in number.
Why should we benchmark and improve ImageNet robustness?
The first of which showed that many years of architectural advance- ments corresponded to minuscule changes in relative corruption robustness. Therefore benchmarking and improving robustness deserves attention, especially as top-1 clean ImageNet accuracy nears its ceiling.
Benchmarking neural network robustness
Network for communications over distance
A telecommunications network is a group of nodes interconnected by telecommunications links that are used to exchange messages between the nodes. The links may use a variety of technologies based on the methodologies of circuit switching, message switching, or packet switching, to pass messages and signals.
A telecommunications network is a group of nodes interconnected by telecommunications
Network for communications over distance
A telecommunications network is a group of nodes interconnected by telecommunications links that are used to exchange messages between the nodes. The links may use a variety of technologies based on the methodologies of circuit switching, message switching, or packet switching, to pass messages and signals.
