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Found 6 papers, 0 papers with code
Targeted Adversarial Attacks on Generalizable Neural Radiance Fields
Neural Radiance Fields (NeRFs) have recently emerged as a powerful tool for 3D scene representation and rendering.
On the Feasibility and Generality of Patch-based Adversarial Attacks on Semantic Segmentation Problems
Deep neural networks were applied with success in a myriad of applications, but in safety critical use cases adversarial attacks still pose a significant threat.
Mitigating the Bias of Centered Objects in Common Datasets
Convolutional networks are considered shift invariant, but it was demonstrated that their response may vary according to the exact location of the objects.
Filtered Batch Normalization
It is a common assumption that the activation of different layers in neural networks follow Gaussian distribution.
Application-level Studies of Cellular Neural Network-based Hardware Accelerators
While it is well-known that CeNNs can be well-suited for spatio-temporal information processing, few (if any) studies have quantified the energy/delay/accuracy of a CeNN-friendly algorithm and compared the CeNN-based approach to the best von Neumann algorithm at the application level.
A mixed signal architecture for convolutional neural networks
Deep neural network (DNN) accelerators with improved energy and delay are desirable for meeting the requirements of hardware targeted for IoT and edge computing systems.
