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one-shot learning advances. Synthesize training data to improve classification - A VAE with an architecture adequately tuned provides better data over- sampling/synthesis results than alternative methods (SMOTE, ADASYN...) - Very simple encoder and decoder networks (3 layers only) are enough to obtain best results. Increasing the number of layers does not improve results. - Using a conditional VAE instead of a VAE provides many advantages in terms of the ability to generate features conditioned on the classification labels. -VAEs and conditional VAEs present robust and easier training than alternatives that do not use variational methods. Synthesize missing data Table 8. Lessons learned Doctoral Thesis: Novel applications of Machine Learning to NTAP - 60PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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