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About Evaluating Resilience of Deep Learning Models: Evaluando La Resiliencia de Modelos de Deep Learning
This document evaluates the resilience of three deep learning models - VGG19, ResNet50, and InceptionV3 - when faults are injected during execution. A fault injection program was developed that could introduce faults into the weights of layers with varying failure rates. Experiments were run injecting faults with different rates and perturbation types. The results showed that InceptionV3 was the least affected by faults, maintaining stable error rates even as the failure rate increased. VGG19 exhibited the lowest dispersion in error rates across replicas, while ResNet50 and InceptionV3 showed higher variability. The study provides a method to analyze how fault tolerance differs between deep learning models.
Detailed Information
| Author: | ['JhesusErnestoZegarraAlva'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 6 |
| Language: | English |
| Format: | |
| Price: | FREE |
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