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Classification and Protection of Critical Weights in CNNs
DescriptionConvolutional neural networks (CNNs) are being incorporated into many image-based tasks across a variety of domains. Some of these tasks are real-world safety critical tasks such as object detection and lane line detection for self-driving cars. These applications have strict safety requirements and must be able guarantee the reliable operation of the network. We propose a selective triplication of important parts of the network determined via weight pruning methodologies in order to maintain a reliable CNN in environments that may be resource-limited.
Diversity Equity Inclusion (DEI)
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