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Found 10 papers, 3 papers with code
Self-supervised Robust Object Detectors from Partially Labelled Datasets
With the goal of training \emph{one integrated robust object detector with high generalization performance}, we propose a training framework to overcome missing-label challenge of the merged datasets.
Toward Adversarial Robustness by Diversity in an Ensemble of Specialized Deep Neural Networks
Using MNIST and CIFAR-10, we empirically verify the ability of our ensemble to detect a large portion of well-known black-box adversarial examples, which leads to a significant reduction in the risk rate of adversaries, at the expense of a small increase in the risk rate of clean samples.
Toward Metrics for Differentiating Out-of-Distribution Sets
We empirically verify that the most protective OOD sets -- selected according to our metrics -- lead to A-CNNs with significantly lower generalization errors than the A-CNNs trained on the least protective ones.
Controlling Over-generalization and its Effect on Adversarial Examples Detection and Generation
As an appropriate training set for the extra class, we introduce two resources that are computationally efficient to obtain: a representative natural out-distribution set and interpolated in-distribution samples.
A Principled Approach for Learning Task Similarity in Multitask Learning
Hence, an important aspect of multitask learning is to understand the similarities within a set of tasks.
Controlling Over-generalization and its Effect on Adversarial Examples Generation and Detection
As an appropriate training set for the extra class, we introduce two resources that are computationally efficient to obtain: a representative natural out-distribution set and interpolated in-distribution samples.
Towards Dependable Deep Convolutional Neural Networks (CNNs) with Out-distribution Learning
Detection and rejection of adversarial examples in security sensitive and safety-critical systems using deep CNNs is essential.
Out-distribution training confers robustness to deep neural networks
The easiness at which adversarial instances can be generated in deep neural networks raises some fundamental questions on their functioning and concerns on their use in critical systems.
Robustness to Adversarial Examples through an Ensemble of Specialists
We are proposing to use an ensemble of diverse specialists, where speciality is defined according to the confusion matrix.
Alternating Direction Method of Multipliers for Sparse Convolutional Neural Networks
This method alternates between promoting the sparsity of the network and optimizing the recognition performance, which allows us to exploit the two-part structure of the corresponding objective functions.
