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Found 5 papers, 2 papers with code
Layer-wise Regularized Adversarial Training using Layers Sustainability Analysis (LSA) framework
This paper introduces a novel framework (Layer Sustainability Analysis (LSA)) for the analysis of layer vulnerability in an arbitrary neural network in the scenario of adversarial attacks.
Limited Budget Adversarial Attack Against Online Image Stream
An adversary wants to attack a limited number of images within a stream of known length to reduce the exposure risk.
End-to-End CNN+LSTM Deep Learning Approach for Bearing Fault Diagnosis
Fault diagnostics and prognostics are important topics both in practice and research.
Self-Supervised Representation Learning via Neighborhood-Relational Encoding
Conventional unsupervised learning methods only focused on training deep networks to understand the primitive characteristics of the visual data, mainly to be able to reconstruct the data from a latent space.
Adversarially Learned One-Class Classifier for Novelty Detection
Our architecture is composed of two deep networks, each of which trained by competing with each other while collaborating to understand the underlying concept in the target class, and then classify the testing samples.
