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Found 2 papers, 2 papers with code
A Simple Structure For Building A Robust Model
As deep learning applications, especially programs of computer vision, are increasingly deployed in our lives, we have to think more urgently about the security of these applications. One effective way to improve the security of deep learning models is to perform adversarial training, which allows the model to be compatible with samples that are deliberately created for use in attacking the model. Based on this, we propose a simple architecture to build a model with a certain degree of robustness, which improves the robustness of the trained network by adding an adversarial sample detection network for cooperative training.
HEAM: High-Efficiency Approximate Multiplier Optimization for Deep Neural Networks
We propose an optimization method for the automatic design of approximate multipliers, which minimizes the average error according to the operand distributions.
